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Scientific Graphs and the Hierarchy of the Sciences: A Latourian Survey of InscriptionPracticesAuthor(s): Laurence D. Smith, Lisa A. Best, D. Alan Stubbs, John Johnston, Andrea BastianiArchibaldSource: Social Studies of Science, Vol. 30, No. 1 (Feb., 2000), pp. 73-94Published by: Sage Publications, Ltd.Stable URL: http://www.jstor.org/stable/285770

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s s sABSTRACTStudies comparing the cognitive status of the sciences have long soughtto identify the distinguishing features of 'hard' and 'soft' science. Attempts byphilosophers of science to ground such distinctions in abstract principles and bysociologists of science to detect relevant differences (for example, in consensus levels)have met with limited success. However, recent investigations of scientists' concretepractices of data representation provide new leads on this problem. In particular,Bruno Latour has argued that graphs are essential to science due to their ability torender phenomena into compact, transportable and persuasive form. ApplyingLatour's notion of 'graphism' to the hierarchy of sciences, we found that the use ofgraphs across seven scientific disciplines correlated almost perfectly with theirhardness, and that the same pattern held up across ten specialty fields in psychology.Keywords * data representation * fractional graph area * graphism * hard science* Latour * soft science

Scientific Graphs and the Hierarchy of theSciences:A Latourian Survey of Inscription PracticesLaurence D. Smith, Lisa A. Best, D. Alan Stubbs,

John Johnston and Andrea Bastiani Archibald

Among the most familiar and widespread beliefs about science is that adistinction can be drawn between the 'hard' sciences and the 'soft'sciences. Dating back at least to the writings of Auguste Comte, it has beenthought that the sciences can be arrayed in a hierarchy, with well-developed natural sciences (such as physics) at the pinnacle, the socialsciences at the bottom, and the biological sciences occupying an inter-mediate position.1 A recent demonstration of the continuing influence ofthis widespread belief is provided by Janice Beyer Lodahl and GeraldGordon, who asked scientists to rank different scientific disciplines accord-ing to their level of development. The results neatly mirrored the tradi-tional Comtean hierarchy,with rankingsranging from physics at the top tosocial sciences such as sociology and political science at the bottom.2Given the broad acceptance of the Comtean ordering, it is perhapssurprising that scholars who study science have failed to reach agreementon exactly what characteristics of scientific fields are responsible for theirrelative standing along this dimension of 'hard' and 'soft' science. AmongSocial Studiesof Science30/1(February 2000) 73-94? SSS and SAGE Publications (London, Thousand Oaks CA, New Delhi)[0306-3127(200002)30: 1;73-94;012783]


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Social Studies of Science 30/1

philosophers of science, earlier faith in the logical principles of verifiabilityor falsifiabilityas the relevant distinguishing features has been underminedby a growing realization that the perplexities of the Duhem-Quine problemcall into question the empirical testability of even hard-science theories. Asa result, some philosophers of science, like Stephen Toulmin, have recastthe hard-soft distinction as a matter of relative degrees of disciplinary'compactness',3 while others, like Larry Laudan, have questioned theutility of any distinction between the hard (or mature) sciences and theirsoft (or immature) counterparts. Arguing that sciences never undergo apermanent transition to paradigmatic status in a Kuhnian sense, Laudanwrites that 'it is extremely unclear whether the notion of "mature" sciencefinds any exemplification whatsoever in the history of science'.4 Similarsceptical conclusions have been drawn by other scholars, such as LloydHouser, who declares that 'the "hard science-soft science" notion has beenrevealed as a myth'.5In scientometrics and the sociology of science, efforts to explicate thestatus of disciplines across the Comtean hierarchy have focused on thesearch for measurable correlates of hardness. Some of these studies havelooked for differential rates of progress, as reflected for example in DerekPrice's 'Immediacy Index' of citations, which appeared to show a morerapid rate of obsolescence for papers in the hard sciences than in the soft.6Others have focused on measures of consensus, which were expected to behigher in the harder or more 'codified' sciences.7 Despite some successeswith these approaches, the results have generally been disappointing.Price's Index, which initially appeared to be highly correlated with dis-ciplinary hardness, turned out upon re-analysis to be largely an artefact ofdifferent rates of growth in scientific literatures.8Similarly, it turns out thatsome of the once-promising measures of consensus, such as journalrejection rates, can be attributed in large part to the differential availabilityof journal page-space,9 while others, such as inter-judge reliability injournal refereeing and grant reviewing, simply failed to exhibit the pre-dicted effects. For example, Stephen Cole reported results from sevenseparate investigations designed to ascertain variables that would, acrossdisciplines, correlate reliably with standard notions of disciplinary hard-ness. None turned out to do so, leading Cole to conclude that 'there are nosystematic differences between sciences at the top and at the bottom of thehierarchy in either cognitive consensus or the rate at which new ideas areincorporated'.10Although such conclusions remain controversial," SusanCozzens has noted that such traditional approaches to the problem ofdifferentiatingthe sciences 'have now largelybeen abandoned, for a varietyof reasons', and that quantitative measures of the sort used for standardscience indicators are unlikely to provide breakthroughs. She suggestsinstead that the search for differences can be conducted more promisinglyat the level of scientific specialties, rather than whole disciplines, and byfocusing on the concrete social interactions and practices that scientistsengage in while formulating and reformulating knowledge claims.12

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Smith et al.: Scientific Graphs & the Hierarchy of the Sciences

Cozzens' suggestion is, of course, in line with the turn in sciencestudies awayfrom the earlierglobal focus on theories and toward investiga-tions of the situated practices whereby scientists construct, negotiate andcommunicate scientific facts. These practices include the use of laboratoryinstruments, shared techniques for transforming and analyzing data, thedevelopment of specialized vocabularies and, broadly speaking, the adop-tion of various technologies for representing scientific findings. Suchrepresentational techniques - referred to by Bruno Latour and SteveWoolgar as 'inscription devices' 13- have been viewed by historians andrhetoricians of science as crucial discursive practices for enrolling allies toone's own form of science, and for persuading other scientists of the valueof one's research.14In particular, Latour and Woolgar have characterizedmodern scientific laboratories as organized sites for persuasion by means ofinscription devices.One important form of inscription device that has begun to receiveattention in science studies, including the history and rhetoric of science, isthe scientific graph. In his landmark essay 'Drawing Things Together',Latour laid out the features of graphs that make them an especiallypowerful and persuasive form of inscription. First, they are able to tran-scend scales of time and place, rendering invisible phenomena (such asquarks, ion pumps, gross national products) into easily apprehended icons.Second, they are manipulable, and can be superimposed and compared inways that lead to seeing new connections between seemingly unrelatedphenomena, discerning similarities between events vastly separated in timeand place, and assessing the congruence of empirical and theoreticalcurves. As such, they encourage the sort of abstraction from detail togeneralities that is characteristic of theoretical science. Third, graphs are'mobile' or transportable: they can be carried from laboratory to labo-ratory, or from laboratories to scientific conferences, or from research sitesto sites of application. Fourth, they are 'immutable', both in the sense offixing the flux of phenomena - and thus stabilizing what may be onlyephemeral in nature or the laboratory - and in the sense of remainingstable as they are transported across contexts. Fifth, as 'immutable mo-biles', graphs can be enlisted in the task of persuading scientists incompeting camps of the validity of one's evidence. As Latour puts it, awell-constructed graph raises the cost of dissenting from one's own fa-voured viewpoint, forcing scientific adversaries to muster their own evi-dence in the form of even better graphs. To the extent that scientists areable to mobilize consensus on data and evidence, it is through competitionand negotiation over graphical representations (hence Latour's motto that'inscriptions allow conscription').The centrality and pervasiveness of graphsin science led Latour to conclude that scientists exhibit a 'graphicalobsession', and to suggest that, in fact, the useof graphsis what distinguishesscience rom nonscience.'5Others who analyze the representational practicesof scientists share Latour's conviction that graphical displays of data play acentral rather than peripheral r6le in the process of constructing andcommunicating scientific knowledge.16

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On the face of it, the strikingclaim that graph use distinguishes sciencefrom nonscience may appear hyperbolic, but Latour argues that largeeffects - such as the powerful role of science in Western culture - can infact arise from small-scale, local practices of knowledge production that areconsistently applied. In his view, earlier efforts to demarcate science fromnonscience, especially those arising in the philosophy of science, weremisguided in their search for large causes for large effects; in looking fordemarcation in all the wrong places (such as the 'logic' of science),philosophers neglected the evidence before their eyes that could be foundthrough the ethnography of everyday scientific practice. In Latour's ownethnographic studies, he observed that when disagreements arose over thenature of phenomena and their interpretation, scientists invariablyrevertedto the use of graphical displays, even if only scribbled on a cocktail napkin,in order to negotiate laboratory facts. So strong was their dependence ongraphs that they often found themselves dumbstruck when deprived ofaccess to graphical materials to help present their case.17If Latour is right in his claim that graphs are essential to science - that'graphism' is the distinguishing feature of science - his thesis has research-able implications for the question of how to understand the differencesbetween supposedly well-developed, high-consensus fields (such as phys-ics) and what are thought to be less-developed, low-consensus fields (suchas sociology). In general, we would expect the 'harder' sciences to exhibit ahigher rate of graph use than the 'softer' sciences, and that such differencesmight also appear at the level of specialty fields within disciplines. Al-though novel, this notion may actually cohere with certain longstandingbeliefs about the hierarchy of the sciences. For example, if the hardsciences make more use of graphs than the soft sciences, then the intuitionsof many that hard sciences enjoy higher degrees of consensus, and workwith phenomena that are more stable and clearly defined, might have aLatourian explanation: the unique capacity of graphical displays to renderphenomena into transportable yet immutable representations tends toforge consensus of scientific belief. As Latour himself suggests, 'to go from"empirical"to "theoretical" sciences is to go from slower to faster mobiles,from more mutable to less mutable inscriptions'.18In this paper, we present evidence bearing on this issue of how'graphism' relates to the hierarchy of the sciences. We do so first bypresenting a re-analysis of existing data on graph use across seven scientificdisciplines; then we extend the analysis to an original archival study ofgraph use across journals in ten subfields of psychology.

Graph Use Across the SciencesIn an extensive study of scientists' use of graphs in various disciplines,William Cleveland surveyed articles in scientific journals from the years1980-81.19 For each discipline, four journals were surveyed (or five in thecase of economics and physics), with 50 articles randomly drawn fromeach journal.20As a measure of graph use, he recorded 'fractional graph

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Smith et aL: Scientific Graphs & the Hierarchy of the Sciences

area' (FGA), which represents the proportion of the total page area inarticles devoted to graphical data displays. Figure captions were excludedin computing the area of graphs, so that FGA represented, in effect, theamount of text that was displaced by graphs. This page-space measurereflects the common understanding that journal space is a crucial limitedresource in the sciences, and hence that whatever occupies it is a valuablecommodity. Cleveland defined 'graphs' as figures that have scales andconvey quantitative information. Included under this definition are graphssuch as scatter plots, line graphs, time series graphs, dot plots andhistograms, as well as bar charts (which have one scale at a nominal orordinal level of measurement and a second scale at an interval or ratiolevel); maps were counted as graphs only if they conveyed statisticalinformation other than geographical location (for example, colour codingof regions by population density). Excluded under Cleveland's definitionare figures such as apparatus illustrations, theoretical diagrams and flowcharts.21The results, reported in Figure 3 of Cleveland's paper, revealed thatnatural science journals tended to have much higher FGAs than socialscience journals. In fact, the mean FGA for the hard disciplines - physics,chemistry, medicine and biology - was 0.14, whereas the mean FGA forthe soft disciplines - psychology, economics and sociology - was 0.03.22AsCleveland noted: 'Clearly graph usage is much greater among the naturalscience journals than among the social science . . . journals'.23Furtheranalysis indicated that these differences were not due to differences in thesizes of graphs, but rather to differences in their number. Moreover, theseobserved differences in graph use did not merely reflect the presence orabsence of data in the articles surveyed, but rather the means by which thedata were presented. Cleveland observed that 'many of the social sciencejournals have much data yet make very little use of graphs', an observationin line with the prevalence of tables as the primary means of datapresentation in most social science journals.24To gain a more detailed view of the possible relation between graphuse and disciplinary hardness, we asked a group of 36 respondents (psy-chologists and psychology doctoral candidates at the University of Maine)to rate the hardness of seven disciplines for which Cleveland had collectedFGA measures. The instructions on the rating sheets began as follows:

It is commonlybelievedin our culturethat a distinctioncan be drawnbetween the 'hard'sciencesand the 'soft' sciences.Althoughthese cate-goriesarenot alwaysclear-cut,most peoplehavesome sense of whatthehard-softdistinctionmeans.In the surveyyou arebeingaskedto fillout,we are interested n your impressionsof which areasof science can beconsideredrelatively ardandwhich canbe considered elativelyoft.

The respondents were then asked to rate each of the disciplines on a10-point Likert scale, with 1 representing the soft end and 10 the hardend.The resulting mean ratings for the disciplines were as follows: physics(9.35), chemistry (8.85), biology (7.95), medicine (7.15), psychology

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(6.15), economics (5.10) and sociology (3.39). These ratings conform veryclosely to the rankings provided by Lodahl and Gordon's respondents,confirming that belief in the Comtean hierarchy of sciences is widespreadand consensual. As a further check on the validity of our hardness ratings,they were also compared with two other measures of the hardness ofdisciplines: (a) Hanna Ashar and Jonathan Shapiro's index of 'paradigmdevelopment',25which is a rankingof hardness based on three field-specificmeasures (length of dissertation abstracts, length of dissertations, andthe longest course-chain of prerequisites for upper-level undergraduatecourses in the field); and (b) Anthony Biglan's hard-soft dichotomy,26 abinary variable derived from multidimensional scaling of similaritiesbetween fields.27 For the six fields for which there were data on all fourmeasures (medicine was not included in the three comparison measures),the correlations with our respondents' hardness ratings were: 0.94 forLodahl and Gordon's rankings (Spearman rho); 0.94 for Ashar andShapiro's paradigm development measure (Spearman rho); and 0.91 forBiglan's hard-soft dichotomy (point biserial correlation).28In Figure 1, Cleveland's measures of graph use (mean FGAs) for theseven disciplines are plotted against the hardness ratings. As can be seen,the FGAs ranged from a low of 0.01 for sociology to a high of 0.18 forchemistry, and the correlation between hardness and graph use was nearlyperfect (Pearson r = 0.97, p < .01). The sole deviation from monotonicityin the relationship was physics, which had a slightly lower FGA (0.17) thanchemistry (0.18) despite having a slightly higher hardness rating. Thesefindings, although preliminary, clearly support Latour's thesis that graphFIGUREGraphUse as a Functionof the Rated Hardnessof Seven ScientificDisciplines

0.2 -_ - /- Chemistry^r= 97 A Physics0.16 - /

, ^i Biologyc o0.12 -O ~- - Medicine

C 0.08-0 /0 . 0 - Psychology3 0.04 -L / EconomicsLL* / Sociology0 - /

2 3 4 5 6 7 8 9 10Rated Hardness

Source: Data from Cleveland, op. cit. note 19.

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use is the hallmark of science. Not only is the relationship between graphuse and disciplinary hardness a strong one, but the magnitude of thedifferences is substantial, approaching a 20:1 ratio in FGAs in the case ofsociology and chemistry. On the whole, these results would seem towarrant further investigation, and suggest that the current disillusionmentwith earlier efforts to find quantitative correlates of hardness at the level ofdisciplines may be premature.29Graph Use Across Subfields of PsychologyIn view of the promise shown by graph use as a correlate of disciplinaryhardness, we subjected Latour's thesis of graphism to a further and morestringent test. It is well known, at least among the scientists involved, thatthe hierarchy of sciences is mirrored within disciplines, in that the varioussubdisciplines making up a field of study are commonly regarded asexhibiting differing levels of hardness. In physics, for example, particlephysics is usually viewed as being more prestigious and better-codified -that is, harder - than solid-state physics. In the biological sciences, molec-ular biology and cell biology are viewed as having higher status in thesubdisciplinary hierarchythan systematics or ecology.30Similarly, psychol-ogists routinely regard such fields as physiological psychology or experi-mental cognitive psychology as harder than (say) social psychology oreducational psychology.If graph use is in fact a representational practice intimately related tothe hardness of scientific fields, we would also expect it to correlate highlywith measures of subdisciplinary hardness - that is, at the level of bothwithin- and between-discipline differences. As an initial attempt to in-vestigate this possibility, we examined subfield differences in psychology.The same respondents who had rated the disciplines for hardness were alsoasked to rate the 25 journals published by the American PsychologicalAssociation in terms of the hardness of the subfield represented by each(again on a 10-point Likert scale). These ratingswere compiled, and the 25journals were ranked according to their mean hardness rating. Two jour-nals were then chosen from each quintile of hardness ratings, and the tenselected journals were surveyed for their use of graphs.31For each of theten (with one exception), fractional graph area was measured for 16randomly sampled articles from the period 1980-95, with four drawn fromeach of the years 1980, 1985, 1990 and 1995.32 For the resulting 156articles, FGA was measured following exactly the procedure used byCleveland.Table 1 presents the numerical ratings of hardness for the journals. Asexpected, journals representing the biological and experimental areas ofpsychology (for example, BehavioralNeuroscience,Journal of ExperimentalPsychology)were consistently rated as harder than those journals that focuson social and educational phenomena (such as Journal of CounselingPsychology,Journal of EducationalPsychology).This finding parallels, at thelevel of subdisciplines, both Comte's conception and Lodahl and Gordon's

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Social Studies of Science 30/1TABLE1Hardness Ratings for Ten Psychology JournalsJournal Rated HardnessBehavioral Neuroscience 8.77Journal of ExperimentalPsychology:Animal Behavior Processes 7.69Journal of ExperimentalPsychology:General 6.91DevelopmentalPsychology 6.06Journal of ComparativePsychology 5.97Journal ofAbnormalPsychology 5.53Journal of Personalityand Social Psychology 5.18Journal of Consultingand ClinicalPsychology 4.93Journal of EducationalPsychology 3.67Journal of CounselingPsychology 3.46

findings. Thus it seems that the notion of a hierarchy reproduces itselfwithin scientific fields, at least in the case of psychology. Interestingly, themean rating for the 10 journals represented inTable 1 (M = 5.82) fell closeto the mean rating for the discipline of psychology (M = 6.42) as derivedfrom the earlier discipline-rating task. This suggests not only that theselected journals are typical of the discipline (at least in terms of perceivedhardness), but also that the raters were using the rating scale consistently,regardless of whether journals or disciplines were being rated.33The results of the graph-use survey showed that the mean FGA for the156 articles in the 10 psychology journals was 0.046; this means that,overall, about one-twentieth of the page space in these articles was devotedto graphical displays.This overall mean FGA accords well with Cleveland'sfinding of a mean FGA of 0.053 for the four psychology journals hesurveyed (only one of which, the Journal of ExperimentalPsychology,was inour sample). The results also showed, again as expected, that the harderareas within psychology (such as physiological psychology) exhibitedhigher graph use than the softer areas (such as social psychology). Thehighest FGA among the 10 journals was in BehavioralNeuroscience M =0.118), a value that approaches the mean for biology (M = 0.128) inCleveland's data.The lowest FGA was in the Journal of CounselingPsychol-ogy (M = 0.007), which falls near Cleveland's mean of 0.01 for sociology.The use of graphs as inscription devices in psychology thus appears to spana large range, just as the field itself encompasses subfields ranging fromthose closely allied with biology to those nearer the social sciences.As to the crucial issue of whether the relationship between graph useand hardness holds within a single discipline, Figure 2 shows that it isagain nearly linear: the correlation between rated hardness and FGA isr = 0.93, p < .01. These results within psychology closely mirrorCleveland's findings for graph use in science at large: our results forpsychology indicate that the harder subfields tend to devote more space tographs than do the softer areas.34

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FIGUREGraph Use as a Function of the Rated Hardness of Ten Journals in Psychology

0.14 . .. .. . . .m 0.12 -BNSaJ~ r=.93I..LzC~~~~~~~- _ /. JEP:ALB< 0.1 - / -

Q.cT 0.08-(' -^,,/ ~ COMP

oS 0.06 - */ JEP:G0 DPI 0.04 - /o JAP.^L~~~n-> 3~~~~JPSPL 0.02 - / JCCPJCCP- */ - JEdP0- / JCP

2 3 4 5 6 7 8 9 10Rated Hardness

See Table 1 for full titles of journals.

Psychology in the Hierarchy: Graphism as an IntegratingConceptAs noted above, the diverse discipline of psychology appears to mirrorthe hierarchyof the sciences as a whole, even as it holds a place within thathierarchy. For this reason, it may be instructive to examine its place inthe hierarchy in more detail. One way to situate it would be to interpolateour findings on graph use in psychology into Cleveland's findings for thedisciplines. This is done graphically in Figure 3, where the disciplinesstudied by Cleveland are arrayedhorizontally according to the rank orderof their hardness, and the ten psychology journals, also in rank order ofhardness, are placed in the position of psychology as a whole. The unifiedvisual impression produced by the resulting upward sweep of data points issuggestive, for it vividly conveys the manner in which psychology bridgesthe span between the soft and hard disciplines, overlappingin its graph usethe sciences of sociology and economics at one end, and the biomedicalsciences at the other. This display lends credence to the notion that graphs,although not fully universal to science, at least provide a potentiallyuniversal index of the 'scientificity' of fields and subfields across science'shierarchy. By depicting the hierarchy of psychology within this largercontext, the display also suggests one possible resolution of the issue ofwhether aspects of the cognitive structure of science that apply at thediscipline level also apply at the specialty level; and it implies that theabandonment of one level in favour of the other may be premature.Because Figure 3 shows graph use as a function only of rankeddisciplines and journals, with psychology journals interpolated in the array

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Social Studies of Science 30/1FIGUREGraph Use in Six Disciplines and Ten Psychology Journals, as a Function of RankedHardness

0.2 ,, ,,,,,,,, , , , ,0 0c) 0.15 -

-& - . 0 -

005-0 0.1-

0 *0

0 L. * i *i

DisciplineorJournalThe psychology journals are placed in the position occupied by psychology in Figure 1.

of sciences, it involves discontinuities in the actual hardness ratings. Forexample, the Journal of CounselingPsychologyappears to the right of eco-nomics, even though its hardness rating actually fell below that ofeconomics. Thus, a metrically preferable way to display the integratedfindings of the two graph-use surveys would be to present the FGAs as afunction of the actual numerical ratings: this is done in Figure 4. LikeFigure 3, this graph conveys the close relationship between rated hardnessand graph use, regardless of an item's status as a discipline or a journalrepresenting a subdiscipline. The correlation between rated hardness andFGA for the six disciplines and ten journals, taken together, is 0.94,p < .01.35DiscussionOn the face of it, our findings support Latour's view that graphs - with alltheir virtues as immutable and immobile inscription devices - are crucialto the scientific enterprise. The use of graphs, as measured by the propor-tion of journal page space devoted to them, appears to be a sensitive indexof the hardness of scientific fields, whether at the level of entire disciplinesor at the level of specialty subfields. Considering that page space is a

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FIGUREGraph Use in Seven Disciplines and Ten Psychology Journals, as a Function of RatedHardness

0.2 , , . .0* Psychology Journals

0.16 - o Scientific Disciplines0

o0.12 -0 - 0- 0.08-0I o0.04- *o ?- . *,*.., r=.94 -

0 ! , I U. I i i2 3 4 5 6 7 8 9 10Rated Hardness

precious commodity, FGA has a good deal of face validity as an indicatorof the importance of graphs, and would seem to deserve a place among theindicators used in future studies of the cognitive status of scientific fields.One way to put the present results into perspective is to compare themwith the results of previous work on correlates of disciplinary hardness. Asnoted earlier, attempts by sociologists of science to isolate quantitativeindices of hardness have met with little success. In reviewing the literature,we found more than 20 variables that have been proposed as indicators ofhardness and subjected to empirical investigation.36 Fully one third ofthese variables (7 of 21) failed even to correlate in the predicted directionwith our measure of rated hardness. Of the remaining 14 measures thatwere at least in the expected direction, only two were correlated stronglyenough with hardness ratings to reach statistical significance. These werejournal rejection rate,37and citation concentration by author.38 Of thesetwo, only Cole's measure of citation concentration showed a correlation asstrong as that reported here for FGA. The use of FGA as an index ofhardness thus compares favourably with the performance of other pro-posed indices, underscoring our conclusion that graph use is a viable andpromising indicator of the hierarchical status of scientific disciplines.For all of the suggestiveness of the present findings, however, they areonly preliminary, and are based on limited samples. Although Cleveland'sdata were drawn from 200 articles in each discipline (250 in the case ofeconomics and physics), the samples came from only four or five journalsin any one discipline, and it remains uncertain how representative those

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journals are. Similarly, our data for psychology were limited to relativelysmall samples of articles in each of ten journals; and although the journalslikely represent those published by the American Psychological Associa-tion, APA journals may differ systematically from those found in thediscipline as a whole. Still, the strength of the observed relationships wouldwarrant further use of the FGA measure, particularlygiven its potential toprovide an integrated understanding of representational practices acrossthe spectrum of disciplines and subfields (as seen in Figures 3 and 4).Further studies would help assess the generality of our results, especiallystudies of diverse disciplines, such as biology, that comprise a wide rangeof specialties varying in their degree of scientificity.39

Other questions of generality arise from the fact that our sample, likeCleveland's, was taken from a limited time period. Obviously, a historicaldimension to graph use remains to be explored. As a number of com-mentators have noted, the boundaries between hard and soft science arefluid and historically conditioned,40 and there is little reason to presupposethat graph use in the various sciences has remained stable over time. In oneof the few studies relevant to this issue, Charles Bazerman found thatarticles on spectroscopy in the Physical Review made increasing use ofgraphs over the years 1893-1980, a period during which spectroscopy wasbeing increasingly codified under the theoretical rubric of quantummechanics.4' The study of scientific practices that accompany the codifi-cation of fields from soft into hard - including inscription practices such asthe use of graphs - would appear to offer a rich field for historicalanalysis.42A more difficult issue is the interpretive problem of whether FGAcould be an artefact or epiphenomenon of some other general character-istic that distinguishes the hard and soft sciences. In other words, therelationship between graph use and hardness may not be specific to graphuse, but rather reflect another underlying variable. One obvious candidatefor such an alternative interpretation would be the degree to whichscientific fields work with quantitative data.Some such interpretation of our data is prima facie plausible, but thereis reason to suspect that it is faulty. As noted earlier, Cleveland concludedthat the differences he observed in graph use could not be attributed to theabsence of numerical data in soft-science journals, which he found to beplentiful. Informal observation of our sample of psychology journals indi-cated that the soft-psychology journals were heavily laden with quantitativedata, but that the data were usually presented in the form of tables;differences in graph use did not appear to correlate with the amount ofdata presented. These impressions could be made more precise, of course,and the question of the specificity of the relation of graph use to hardnesscould presumably be resolved empirically by measuring table use in amanner analogous to Cleveland's FGA.43Indeed, a profitable direction forfuture research on inscription practices would be to quantify the relativeuse of graphs and tables across the spectrum of sciences.

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There are further reasons to suppose that variations in graph use arenot simply a manifestation of disciplinary differences in reliance on quanti-tative data. Many authors have observed that the soft sciences typicallyoffer masses of quantitative data, sometimes to the point of being swampedin them. Fritz Machlup, for example, points out that:

... if the availabilityf numericaldata werein andof itselfan advantagein scientific investigation, economics would be on the top of all sciences.Economics s theonlyfieldin whichthe rawdataof experiencearealreadyin numerical form.44As we have seen, economics nonetheless ranks low both in hardness and ingraph use. More generally, in assessing the status of quantitative socialscience, George Bohrnstedt concluded that 'quantification alone is notsufficient for the development of a viable social science'.45Ian Hacking hasalso argued that what the social sciences lack in hardness cannot beattributed to their inability to generate and manipulate numerical data:

Social scientistsdon't lackexperiment; hey don't lackcalculation;heydon't lackspeculation; heylackthe collaboration f the three.46The common point here is that masses of data, however carefully collectedand assembled, do not in themselves yield immutable facts or spontane-ously evince relationships to theory.If 'graphism' in science is not simply a reflection of a field's level ofquantification, then what doesaccount for the relationship between graphuse and the hardness of fields?Although there is no univocal answer to thisquestion at present, we concur with Latour (and others who study repre-sentational practices) in suggesting that graphs play the crucial r6les ofstabilizing facts and relating data to theoretical formulations, performingthe function that Bazerman calls 'theoretical integration'.47As noted byFrancoise Bastide, tables, the common mode of data display in the softsciences, generally lack this capability:natural scientists prefer to avoid theuse of tables because they are perceptually inefficient, rhetorically un-persuasive, and often 'perfectly undecipherable'.48This conclusion is sup-ported by Bazerman's finding that as the field of spectroscopy maturedthrough the 20th century, the use of tables declined, and that the field'scodification was accompanied by an increased use of graphs containingmultiple panels and multiple curves, making possible direct comparisonsof theoretical and empirical values in a way that was efficient and persuas-ive.49As Michael Lynch argues, graphs are 'revelatory objects' that 'simul-taneously analyze what they reveal'.50These comments suggest that graph use is a concomitant of theoreticaldevelopment, and indeed the task of relating data to theoretical valuesappears to be one important function of graphs. Ronald Giere, for exam-ple, has noted that nuclear physicists routinely assess the fit between dataand theoretically derived values by plotting the data against theoreticalpredictions and making informal judgments of the degree of fit. When

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Giere queried a physicist about why statistical tests for goodness of fit arenot used in physics, the reply was: 'More kinds of data can be assimilatedby the eye and brain in the form of a graph than can be captured with2'.51 Such a remark clearly evinces the role (as discussed by Latour andBazerman) that graphs play in relating observations to theoreticalformulations.

Yet, even in non-theoretical scientific contexts, graphs can be used todiscover, summarize and stabilize empirical relationships, serving the'revelatory' function alluded to by Lynch. Examples of such non-theoretical uses of graphs are not hard to find in the history of science.Thomas Hankins has recounted how the Harvard physiologist L.J.Henderson used a form of graphs called 'nomograms' to represent thecomplex interactions of the components of blood, in the absence of anyformal theory of those interactions.52 Similarly, Frederic Holmes andKathryn Olesko have shown that Helmholtz's path-breaking measurementof the speed of the neural impulse depended critically on his use ofgraphical methods (as Helmholtz himself understood); again, this wasachieved in the interest of demonstrating a single (although important)fact, without guidance from any particular theory of neural functioning.53Between the stages of fact-construction and the higher-order relatingof data to pre-existing theories, graphs may also play other important r6les,most notably in the process of formulating tentative theories. According toHankins, the physicist Willard Gibbs 'began his work in thermodynamicswith the stated purpose of creating a better graph', and indeed Gibbs' firstpublication in thermodynamics was devoted to the use of graphicalmethods in that field; it was the construction of an adequate graphical rep-resentation that then allowed him 'to mathematize and transform the entirescience of thermodynamics in a profound way'.54 At a more mundanelevel, Roger Krohn has described how biologists attempting to improveexisting models of algal bloom in lakes proceeded with their task byinspecting time-series graphs, so as to identify new causal factors to beincorporated in revised theories.55As Russ Hanson has argued, the stepfrom familiarity with relevant data to the formulation of a hypothesisbecomes less mysterious once one recognizes that the transition from datato theory is largely a matter of pattern recognition guided by tentativeconcepts.56Among the tools available to scientists, graphs would seem tobe especially, perhaps uniquely, well suited to the detection and recogni-tion of such patterns.57It thus appears that if graphs are crucial to science,their power is not achieved in any single way; rather, their importancewould seem to stem from their use in at least three contexts - fact-construction, theory-testing, and the intermediate process of theory-formation.58

Although the attempt to attribute the power of graphs to any singler6le they play in science seems dubious, legitimate questions can be raisedabout the relative frequency with which graphs function in various ways toachieve their effects. The alleged rhetorical and integrativepower of graphscan presumably be tested by empirical means, and the particularroles they

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play may well differ systematically from one field to another. For example,if Latour is correct about the rhetorical force of graphical inscriptions,scientific papers that make extensive use of graphs should generally enjoyhigher citation rates than those which do not. Moreover, papers that usegraphs chiefly for theoretical integration, as indicated by their use of boththeoretical and empirical curves, should be especially well-cited in journals(such as Physical Review and PsychologicalReview) that specialize in pub-lishing theoretical articles, and in fields that enjoy relatively high levels oftheoretical codification. On the other hand, papers using graphs so as toreveal and depict novel empirical relationships between variables would beexpected to receive citations chiefly in journals devoted to reports ofempirical research in fields that lack strong theoretical integration.59The conclusion that graphs constitute the lifeblood of science shouldperhaps not be surprising, as those who have studied the concrete practicesof scientists pursuing research and constructing knowledge-claims havelong been aware of the power of graphical displays. David Gooding, forexample, has documented how the mathematically untutored MichaelFaradaywas able to explore and solve difficult problems in electrical theorysolely through graphical means.60Likewise, Gray Funkhouser's history ofscientific graphs makes clear that they have long played a critical role inscientific praxis and communication.61As Michael Lynch has put it:

[VJisualdisplaysare distinctively nvolved in scientific communicationand in the very'construction' f scientific acts .... Suchrepresentationsconstitute he physiognomy f the objectof the research.62As the technologies of 'virtual witnessing' have evolved along with thegenre of scientific writing, methods of graphical display have come toencompass a wide range of techniques for enlisting allies through what JanGolinski calls 'ocular proof'.63That the use of such methods has not beenevenly distributed across the spectrum of the sciences may well speakdirectly to the perennial question of why the harder sciences seem toexperience higher degrees of facticity and theoretical integration than theirsofter counterparts.64NotesPortions of this paper were presented at the Maine Biological and Medical SciencesSymposium (Waterville, ME, May 1997) and at the annual meeting of the AmericanPsychological Association (Boston, MA, August 1999). We thank Steven Cohn, Linda Silka,Bruno Latour and three anonymous referees for their critical reading of the manuscript andtheir helpful suggestions.1. Auguste Comte, ThePositivistPhilosophyofAugusteComte,Vol. 1, trans. HarrietMartineau (London: George Bell & Sons, 1896, first published 1830-42).2. Janice Beyer Lodahl and Gerald Gordon, 'The Structure of Scientific Fields and the

Functioning of University Graduate Departments', AmericanSociologicalReview,Vol. 37(1972), 57-72.3. Stephen Toulmin, Human Understanding:The CollectiveUse and Evolutionof Concepts(Princeton, NJ: Princeton University Press, 1972), 378-95.

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Social Studies of Science 30/14. Laurens Laudan, Progressand Its Problems:Towards Theoryof ScientificGrowth

(Berkeley: University of California Press, 1977), 151.5. Lloyd Houser, 'The Classification of Science Literatures by Their "Hardness"', Library& InformationScienceResearch,Vol. 8 (1986), 357-72, at 367. See also Karin D. Knorr-Cetina, 'Social and Scientific Method, or What Do We Make of the DistinctionBetween the Natural and the Social Sciences?', Philosophyof the Social Sciences,Vol. 11(1981), 335-59.6. Derek J. de Solla Price, 'Citation Measures of Hard Science, Soft Science, Technology,and Non Science', in Carnot E. Nelson and Donald K. Pollack (eds), CommunicationAmong Scientistsand Engineers Lexington, MA: D.C. Heath, 1970), 3-22.7. Harriet Zuckerman and Robert K. Merton, 'Age, Ageing, and Age Structure inScience', in Merton, ed. Norman W. Storer, TheSociologyof Science(Chicago, IL: TheUniversity of Chicago Press, 1973), 497-559.

8. Stephen Cole, Jonathan R. Cole and Lorraine Dietrich, 'Measuring the Cognitive Stateof Scientific Disciplines', inYehuda Elkana, Joshua Lederberg, Robert K. Merton,Arnold Thackray and Harriet Zuckerman (eds), Toward Metricof Science(New York:JohnWiley & Sons, 1978), 209-51.9. Janice M. Beyer, 'Editorial Policies and Practices Among Leading Journals in FourScientific Fields', SociologicalQuarterly,Vol. 19 (1978), 68-88. See also the review ofthis issue in Stephen Cole, Making Science: Between Nature and Society(Cambridge,MA: Harvard University Press, 1992), 111-16.10. Stephen Cole, 'The Hierarchy of the Sciences?', AmericanJournal of Sociology,Vol. 89(1983), 111-39, at 111.11. For example, the attribution of differences in rejection rates to page-space differenceshas been disputed by Lowell L. Hargens, 'Cognitive Consensus and Journal RejectionRates', AmericanSociologicalReview,Vol. 53 (1988), 139-51.12. Susan E. Cozzens, 'Comparing the Sciences: Citation Context Analysis of Papers fromNeuropharmacology and the Sociology of Science,' Social Studiesof Science,Vol. 15,No. 1 (February 1985), 127-53.13. Bruno Latour and Steve Woolgar, LaboratoryLife:The Construction f ScientificFacts(Princeton, NJ: Princeton University Press, 2nd edn, 1986), esp. Chapter 2.14. See, for example, Alan G. Gross, TheRhetoricof Science(Cambridge, MA: HarvardUniversity Press, 1990), 74-80; Caroline A. Jones and Peter Galison (eds), PicturingScience,ProducingArt (New York:Routledge, 1998); and Timothy Lenoir (ed.),InscribingScience:ScientificTextsand theMaterialityof Communication Stanford, CA:Stanford University Press, 1998), esp. Chapters 1 & 13.15. Bruno Latour, 'Drawing Things Together', in Michael Lynch and Steve Woolgar (eds),Representationn ScientificPractice(Cambridge, MA: MIT Press, 1990), 19-68. OnLatour's view that 'inscriptions allow conscription', ee p. 50 (italics in original).16. See, for example, Francoise Bastide, 'The Iconography of Scientific Texts: Principles ofAnalysis', in Lynch &Woolgar (eds), op. cit. note 15, 187-229; Stephen Jay Gould,'Ladders and Cones: Constraining Evolution by Canonical Icons', in Robert B. Silvers(ed.), Hidden Historiesof Science(NewYork: New York Review of Books, 1995), 37-67,at 39-42; Michael Lynch, 'Discipline and the Material Form of Images: An Analysis ofScientific Visibility', Social Studiesof Science,Vol. 15, No. 1 (February 1985), 37-66;and B.H. Mahon, 'Statistics and Decisions: The Importance of Communication andthe Power of Graphical Presentation', Journal of the Royal StatisticalSociety,Vol. 140(1977), 298-323.17. Latour, op. cit. note 15, 22.18. Ibid., 47.19. William S. Cleveland, 'Graphs in Scientific Publications', AmericanStatistician,Vol. 38(1984), 261-69.20. Cleveland did not state how the journals for each discipline were selected, butinspection of his selections suggests that they were intended to represent a range ofsubfields in each discipline. For example, the physics journals include the Journal of

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Physics, Journal of GeophysicalResearch,PhysicalReview (A), PhysicalReviewLettersandJournal ofAppliedPhysics.21. In the survey of graph use in psychological journals (to be described below), wefollowed Cleveland's definition of graphs, and encountered no cases that wereambiguous as to their status as graphs or non-graphs. The vast majority of graphs inthese journals fell into one of the categories just listed, and we encountered no maps,statistical or otherwise, in the sampled articles.22. In addition to these seven traditional scientific disciplines, Cleveland's survey alsoincluded a category of general science journals (e.g. Science)and categories formathematics, statistics, engineering, computer science, education and geography.Thesefields are not included in the present re-analysis of Cleveland's findings, since (with thepossible exception of geography) their status as applied or formal disciplines severelycomplicates their placement in the hierarchy of the sciences. On the need for multipledimensions to characterize the full range of technoscientific disciplines, see AnthonyBiglan, 'The Characteristics of Subject Matter in Different Academic Areas', Journal ofAppliedPsychology,Vol. 57 (1973), 195-203.23. Cleveland, op. cit. note 19, 265.24. Ibid. On the prevalence of tables in social science journals, see the discussion belowand in note 48.

25. Hanna Ashar and Jonathan Z. Shapiro, 'Are Retrenchment Decisions Rational?TheRole of Information in Times of Budgetary Stress', Journal of HigherEducation,Vol. 61(1990), 123-41.26. Anthony Biglan, 'Relationships Between Subject Matter Characteristics and theStructure and Output of University Departments', Journal of Applied Psychology,Vol. 57(1973), 204-13.27. For discussion of these (and other) measures, see Lowell L. Hargens and Lisa Kelly-Wilson, 'Determinants of Disciplinary Discontent', Social Forces,Vol. 72 (1994),1177-95.

28. The close congruence of these measures may be reassuring to those who areunderstandably wary of possible differences between perceivedhardness and objectivelymeasured hardness: see, for example, Cozzens, op. cit. note 12, 130-31. However, theliterature on subjective scaling of social phenomena, such as the seriousness of crimes,shows that such ratings are often closely related to objective measures: see MiltonLodge, Magnitude Scaling: QuantitativeMeasurementof Opinions(Newbury Park, CA:Sage Publications, 1981), Chapters 2 & 3.29. Cole et al., op. cit. note 8; see discussion in Cozzens, op. cit. note 12.30. Warren0. Hagstrom, The ScientificCommunity(New York: Basic Books, 1965),Chapter 4; and Charles C. Davis, 'Biology is Not a Totem Pole', Science,Vol. 141 (26July 1963), 308-10.31. As it turned out, the mean of the hardness ratings for these ten journals (M = 5.82)coincided exactly with the mean for the whole set of 25 journals (M = 5.82),suggesting that the strategy of selecting journals from quintiles of hardness produced arepresentative sample of journals in terms of their relative hardness.32. Because the Journal of ComparativePsychologydid not exist in 1980, only 12 articleswere sampled from it. The journal BehavioralNeuroscience lso did not exist in 1980, sothe year 1983, its first year of publication, was used instead of 1980 (this approachcould not be used for the Journal of ComparativePsychologybecause it was joined with aphysiological journal before 1985). The original rationale for sampling articles acrossthe four 5-year intervals was to allow an assessment of changes in graph use acrosstime, but such changes proved to be minor (see note 42 below).33. Previous research on perceptions of journals provided an opportunity to validate ourrespondents' ratings. In a 1967 study, Leon Jakobovits and Charles Osgood askedmembers of the American Psychological Association to rate 20 psychology journals onvarious semantic differential scales, and then subjected the results to a factor analysis.One of the resulting factors, called 'rigour', was composed of an average of the scalesfor 'scientific-unscientific' and 'rigorous-loose'. For the seven journals that were rated

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Social Studies of Science 30/1both by our raters and by Jakobovits and Osgood's subjects, the correlation betweenthe hardness ratings and rigour scores was r = 0.94, p < .01. This strong correlationboth validates the present ratings and suggests that the hierarchy of hard and softjournals (and presumably of the research areas represented by them) has remainedquite stable over the past three decades. See L.A. Jakobovitsand C.E. Osgood,'Connotations of Twenty Psychological Journals to Their Professional Readers',AmericanPsychologist,Vol. 22 (1967), 792-800.34. This conclusion does not depend on the particular measure of graph use employedhere. In addition to FGA, we scored each article for the number of graphs it containedand for the presence or absence of any graphs.The correlation between graphs/articleand hardness was r = 0.88, p < .01, and the point biserial correlation between hardnessand the presence or absence of graphs was r = 0.94, p < .01. A correlation of 0.93between FGA and graphs/article was also found, suggesting that the journals differed innumber of graphs rather than the size of graphs (as Cleveland found for his cross-discipline differences).35. Inspection of this figure suggests the possibility that the relationship between hardnessand FGA is nonlinear, and indeed an exponential fit to the data produces a least-squares correlation of 0.96. However, the minimal improvement in fit and the absenceof any obvious interpretation of the nonlinearity give no clear grounds for interpretingthe relationship as exponential.36. In this review, the variables we examined were limited to (a) those that were measuredon a ratio or interval scale (so that correlations with rated hardness could becomputed); (b) those for which data were available for four or more disciplines (toavoid spurious correlations due to extremely small samples); and (c) those that hadrelatively direct bearing on the cognitive status of disciplines (a criterion that excluded,for instance, gender differences and differences in political attitudes of scientists acrossdifferent disciplines).37. See Hargens, op. cit. note 11.38. Cole, op. cit. note 9, 125-27.39. This approach is also suggested by Cole et al., op. cit. note 8.40. See, for example, I. Bernard Cohen, Interactions:Some ContactsBetween the NaturalSciences and the Social Sciences (Cambridge, MA: MIT Press, 1994), 6-10, 189-96;Hans Zeisel, 'Difficulties in Indicator Construction: Notes and Queries', in Elkana etal. (eds), op. cit. note 8, 253-58.41. Charles Bazerman, 'Theoretical Integration in Experimental Reports in Twentieth-Century Physics: Spectroscopic Articles in PhysicalReview, 1893-1980', in his ShapingWrittenKnowledge:The GenreandActivity of the ExperimentalArticle in Science (Madison:University of Wisconsin Press, 1988), 153-86.42. Cole et al. (op. cit. note 8, at 249-50) also advocate inclusion of a historical dimensionin the study of science indicators. In the case of graph use, our own data sampled at5-year intervals from the period 1980-95 showed a statistically significant butnumerically slight increase over time; there was no difference between the five hardestjournals and the five softest journals in the rate of increase. For other studies of graphuse across time, see Howard Wainer and David Thissen, 'Graphical Data Analysis',Annual Review of Psychology,Vol. 32 (1981), 191-241; and Darrell L. Butler, 'Graphicsin Psychology: Pictures, Data, and Especially Concepts', BehaviorResearchMethods,Instruments,& Computers,Vol. 25 (1993), 81-92. Both of these studies found increasesin the use of graphs in psychological publications over several decades.43. We recently reported on a preliminary effort in this direction, with results indicatingthat table use is negativelycorrelated with the hardness of subfields in psychology (thussuggesting that hardness is specifically related to graph use rather than to quantificationin general, at least in the case of psychology). See Lisa A. Best, Andrea M. Bastiani,Laurence D. Smith, John Johnston, D. Alan Stubbs and Roger B. Frey, 'Data-Presentation in Hard and Soft Psychology: Graphs and Tables', paper presented at theannual meeting of the American Psychological Association (Boston, MA, August 1999).

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Smith et al.: Scientific Graphs & the Hierarchy of the Sciences44. Fritz Machlup, 'Are the Social Sciences Really Inferior?',Southern EconomicJournal,Vol. 27 (1961), 173-84, at 178.45. George W. Bohrnstedt, 'Social Science Methodology: The Past Twenty-FiveYears',American BehavioralScientist,Vol. 23 (1980), 781-87, at 786.46. Ian Hacking, Representing nd Intervening (Cambridge: Cambridge University Press,

1983), 249. For the broader argument that quantitative methods are not crucial to anyscience, see Randall Collins, 'Why the Social Sciences Won't Become High-Consensus,Rapid-Discovery Science', SociologicalForum,Vol. 9 (1994), 155-77.47. Bazerman, op. cit. note 41, 157, 173 & 183.48. Bastide, op. cit. note 16, 214. The sentiment that tables are undecipherable, which willbe familiar to anyone who has been confronted with page after page of tables, wasexpressed as early as 1891 by the economists Farquhar and Farquhar:'Gettinginformation from a table is like extracting sunlight from a cucumber' (quoted in Wainer&Thissen, op. cit. note 42, 236). On the prevalence of tables in social science, see theclaim in the InternationalEncyclopediaof the Social Sciencesthat 'statistical tables are themost common form of documentation used by the quantitative social scientist': JamesA. Davis and Ann M. Jacobs, 'Tabular Presentation', InternationalEncyclopediaof theSocial Sciences,Vol. 15 (New York: Macmillan, 1968), 497-509, at 497.49. Bazerman, op. cit. note 41, 173.50. Michael Lynch, 'The Externalized Retina: Selection and Mathematization in the VisualDocumentation of Objects in the Life Sciences', in Lynch &Woolgar (eds), op. cit.note 15, 153-86, at 154.51. Ronald N. Giere, ExplainingScience:A CognitiveApproach (Chicago, IL:The Universityof Chicago Press, 1988), 190. Physicists' shunning of statistical tests in favour of'eyeballing' graphs was pointed out earlier by Paul E. Meehl, 'Theoretical Risks andTabular Asterisks: Sir Karl, Sir Ronald, and the Slow Progress of Soft Psychology',Journal of Consultingand ClinicalPsychology,Vol. 46 (1978), 806-34. Meehl hascontinued to advocate the use of graphs over statistics in the social sciences.52. Thomas L. Hankins, 'Blood, Dirt, and Nomograms: A ParticularHistory of Graphs',Isis,Vol. 90 (1999), 50-80, at 50-52, 74-77, 79-80.53. Frederic L. Holmes and Kathryn M. Olesko, 'The Images of Precision: Helmholtz andthe Graphical Method in Physiology', in M. Norton Wise (ed.), TheValuesof Precision(Princeton, NJ: Princeton University Press, 1995), 198-221. For further examples,drawn from psychology, of empirical discoveries that depended on graphical methods,see Laurence D. Smith, Lisa A. Best, Virginia A. Cylke and D. Alan Stubbs,'Psychology Without p Values: Data Analysis at the Turn of the 19th Century', AmericanPsychologist in press). Helmholtz's graphical technique involved instruments for makingrecordings of rapidly occurring muscle contractions, a technique popularized by theFrench physiologist Etienne-Jules Marey. Although not all graph-based discoveries haverelied on instrument-generated graphs (as opposed to hand-drawn statistical graphs),the connection between graphs and instruments in many important cases raises thepossibility that the relationship between graph use and the hardness of scientific fieldsis mediated in part by the differential reliance of fields on instruments. For the viewthat hard science is distinguished from soft science by the former's use of instrumentsthat form a genealogy of research technologies, see Collins, op. cit. note 46.54. Hankins, op. cit. note 52, at 78, 79.55. Roger Krohn, 'Why Are Graphs So Central in Science?', Biologyand Philosophy,Vol. 6(1991), 181-203.56. Norwood Russell Hanson, Patternsof Discovery:An Inquiryinto the ConceptualFoundationsof Science(New York:Cambridge University Press, 1958), 70-73, 82-86. Aclassic example of the role of pattern recognition in theory innovation is the emergenceof the plate tectonics theory of continental drift as a consequence of graphical displaysthat revealed distinctive magnetic patterns in geological strata: see Giere, op. cit. note51, 272. For a broader account of the functions of visual displays in geologicaltheorizing, see Martin J.S. Rudwick, The GreatDevonian Controversy:The Shaping of

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Social Studies of Science 30/1ScientificKnowledgeAmong GentlemanlySpecialists(Chicago, IL:The University ofChicago Press, 1985).57. For an introductory treatment of the human perceptual abilities that underlie the powerof graphs to reveal patterns, see Stephen M. Kosslyn, TheElementsof Graph Design(San Francisco, CA: W.H. Freeman, 1992).58. This conclusion bears on a question raised by two referees who asked whether thepresent findings imply that soft science can be made hard simply by requiring theinclusion of graphs in its publications - say, by editorial decree. The answer to thequestion, at least stated this baldly, has to be 'no'. However, in view of the multiple(and typically beneficial) roles assumed by graphs in science, we find nothingimplausible in the claim that the systematic inculcation of graphic skills and visualthinking among practitioners of soft science would in fact increase the rate of progressin such fields (where 'progress' means increased stability of facts, more rapid discoveryof novel empirical relationships, greater consensus on previously reported empiricalrelationships, more and better-focused attention to relationships between data andtheory, and so on). The training of soft scientists in graphic skills could well lead toimproved ways of planning experiments, structuring data-collection processes, noticingunexpected patterns of findings, thinking about data and trying out hypotheses - inshort, to a visual culture among soft scientists in which graphical practices pervadetheir work. Graphic skills are so taken for granted in the harder sciences that there isno separate curricularprovision for them; they are part of the implicit laboratory praxisroutinely acquired through graduate apprenticeships. That such skills rarely form a partof social scientists' training is a historically conditioned fact, but not one that is beyondmodifying. The simplest answer to the referees' question, then, is that graphs areindeed central to science, but that publishedgraphs are best interpreted as one publicmanifestation (albeit an important one) of an entire set of practices found amongvisually oriented scientists. For documentation of the pervasive use of inscriptions priorto the stage of publication, there are still no better sources than Latour &Woolgar, op.cit. note 13, and Lynch &Woolgar (eds), op. cit. note 15; it is also well to rememberthat Latour's notion of 'graphism' was intended to underscore the pervasiveness ofgraphs in science, and was certainly not limited in scope to published graphs.For examples of writings that explicitly advocate the training of social scientists ingraphical techniques, see William J. McGuire, 'A Perspectivist Approach to theStrategic Planning of Programmatic Scientific Research', in Barry Gholson, William R.Shadish, Jr, Robert A. Neimeyer and Arthur C. Houts (eds), Psychologyof Science:Contributions o Metascience(Cambridge: Cambridge University Press, 1989), 214-45,at 230-31; Armando Machado and Francisco J. Silva, 'Greatness and Misery in theTeaching of Psychology', Journal of the ExperimentalAnalysis of Behavior,Vol. 70(1998), 215-34, at 230-31; and Leland Wilkinson and the Task Force on StatisticalInference, 'Statistical Methods in Psychology Journals: Guidelines and Explanations',AmericanPsychologist,Vol. 54 (1999), 594-604, at 601-602. The latter paper, writtenby an elite task force appointed by the American Psychological Association, concludesits treatment of graphical procedures as follows: 'It is time for authors to takeadvantage of them and for editors and reviewers to urge authors to do so' (602). Thus,to at least one expert committee of social scientists, the promotion of graphism byjournal editors is not a far-fetched idea. For the view that training in graphical skillsshould actually begin in elementary school, see Howard Wainer, 'UnderstandingGraphs and Tables', EducationalResearcher,Vol. 21 (1992), 14-23. Wainer uses the term'graphicacy' to describe such skills, so as to highlight their status as a basic form ofintellectual competence on a par with 'literacy' or 'numeracy'.59. As noted by one referee, further research on how inscriptions are used across the rangeof sciences should bear in mind that visual inscriptions are not limited to graphs. Forexample, surveys could be done of the use of maps, sketches, tracings andphotographs, comparing how these are used in different fields. At least in the case ofpsychology, however, our survey revealed that graphs were by far the most commontype of visual inscription used in the journal literature. In our sample articles, graphs

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Smith et al.: Scientific Graphs & the Hierarchy of the Sciencesoutnumbered photographs by a ratio of 34:1, and non-graph illustrations (for example,conceptual diagrams, flow charts) by more than 7:1. (Similar results were obtained byButler, op. cit. note 42.) Moreover, unlike graphs, neither photographs nor non-graphillustrations exhibited any consistent relationship with the rated hardness of thejournals.60. David Gooding, '"In Nature's School": Faraday as an Experimentalist', in Goodingand Frank A.J.L. James (eds), FaradayRediscovered: ssayson the Life and Work fMichael Faraday, 1791-1867 (NewYork: Stockton Press, 1985), 105-36.61. H. Gray Funkhouser, 'Historical Development of the Graphical Representation ofStatistical Data', Osiris,Vol. 3 (1937), 269-404.62. Lynch, op. cit. note 50, at 153, 154.63. Jan Golinski, Making Natural Knowledge:Constructivism nd the History of Science(Cambridge: Cambridge University Press, 1998), 145. For reviews of the rapidlyexpanding technologies for graphical display, see John M. Chambers, William S.Cleveland, Beat Kleiner and Paul A. Tukey, GraphicalMethods or Data Analysis (NewYork:Chapman & Hall, 1983); Cleveland, VisualizingData (Summit, NJ: Hobart Press,1993); Edward R. Tufte, EnvisioningInformation Cheshire, CT: Graphics Press, 1990);and Wainer &Thissen, op. cit. note 42.64. Given that Latour's classic paper on scientific graphs was the inspiration for the presentinvestigation, it is ironic that Latour himself disregarded possible differences betweenthe hard and soft sciences in terms of their graph use: 'There is no detectabledifference between natural and social science, as far as the obsession for graphism isconcerned': Latour, op. cit. note 15, 39.

Curious readers may like to know that the FGA for our paper, as it has been set in thisjournal, is 0.068. Readers can decide for themselves whether this information is all theyneed to interpret the paper's significance!

Laurence Smith holds a Master's degree in the Philosophy of Science and aPhD in the History of Psychology. He is Associate Professor of Psychology atthe University of Maine, where he pursues research on the history andphilosophy of data analysis practices. His publications include Behaviorismand Logical Positivism (Stanford University Press, 1986) and B.F.Skinner andBehaviorism in American Culture (Lehigh University Press, 1996), co-editedwith William R. Woodward. Email: [email protected] Best is a doctoral candidate in Psychology at the University of Maine.She is conducting experimental research on graph perception, with a focuson the detection of exponential trends in time series data. Email:[email protected] Stubbs is Professor of Psychology and Cooperating Professor of Art atthe University of Maine. His current interests include visual perception, thetheory and practice of data display, 'newmedia' (digital art, web design,etc.), and photography. Email: [email protected] Johnston is a former graduate student in Psychology at the Universityof Maine. His interests are in data display techniques and the socialpsychology of Icelandic dyads. Fax: +1 207 968 2710; email: [email protected]: Department of Psychology, University of Maine, 5742 ClarenceCook Little Hall, Orono, Maine 04469-5742, USA; Fax: +1 207 581 6128.

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94 Social Studies of Science 30/1

Andrea Bastiani Archibald holds a PhD in Developmental Psychology fromColumbia University. She is currently a post-doctoral research fellow at theCenter for Children and Families, Teachers College, Columbia University,where she conducts research (and has published) on adolescent femaledevelopment and the r6le of puberty in the development ofpsychopathology, with special reference to eating disorders.Address: Center for Children and Families, Teachers College, ColumbiaUniversity, New York, New York 10027, USA;fax: +1 212 678 3676; email:[email protected]

smith, et al.(2000) 'scientificgraphs la tour ian survey

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