beyond digital crowdsourcing - how the estonian people’s assembly solved a crisis of democracy

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Beyond digital crowdsourcing - how the Estonian People’s Assembly solved a crisis of democracy Nele Leosk Alexander H.Trechsel European University Institute April 27, 2016

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Beyond digital crowdsourcing -

how the Estonian People’s

Assembly solved a crisis of

democracy

Nele Leosk

Alexander H.Trechsel

European University Institute

April 27, 2016

People’s Assembly

Crowdsourcing

is defined as a public invitation by an actor

or a group of actors for any person or

organization to participate in a process

geared at finding solutions to a societal

problem (adopted Brabham 2008; Howe 2006; Landemore 2014)

Contributions

• Staging the process

• Impact analysis

- Inclusiveness

a) Preconditions

b) Who gets involved?

c) Whose voice is heard?

• Political outcomes

- institutional changes

Political turmoil. Silvergate. 2012

“Charter 12” November 2012

Ice-Cellar meeting. Nov.21, 2012

Crowdsourcing. 7-31.01.2013

Topics

• Financing and financial reporting (of political

parties)

• Political parties and party system

(establishment, membership)

• Public participation in policy making between

the elections (open policy making, petitions,

etc)

• Electoral system regulation

• Political patronage and corruption

• Varia

Analysis. Discussion seminars. Feb-

March 2013

Deliberation day. April 6, 2013

Ideas handed to the

Parliament. April 12, 2013

Crowdsourcing: 1970 registered

users 0

100

200

300

400

Fre

que

ncy

1/7/2013 1/30/2013Day of registration

Gender

Activity by type and gender

Age 0

2040

6080

20 40 60 80 100

Frequency kdensity age, area=1971

47.58

Type of participation Obs Mean Median SD Min Max

Voted 1568 40.43878 13 79.13851 1 1226

Commented 394 7.241117 2 18.74878 1 258

Presented Ideas 644 3.031056 1 12.40387 1 276

05

01

00

150

200

250

Fre

que

ncy

0 20 40 60 80 100Votes (for and against)

05

01

00

150

Fre

que

ncy

0 10 20 30 40Comments

0

100

200

300

400

Fre

que

ncy

0 5 10 15 20 25Ideas

Crowdsourcing. 1969 ideas

by topic

Most endorsed ideas’

presenters

Final ideas’ presenters

Discussion seminars by actors’

group 1. Participation 2. Financing 3. Political

patronage

4. Parties 5. Elections Total

Politician 8

(25%)

10

(29.4%)

9

(30%)

7

(30.4%)

10

(31.3%)

44

(29.1%)

Expert 14

(43.8%)

12

(35.3%)

13

(43.3%)

10

(33.5%)

12

(37.5%)

61

(40.4%)

Citizen 9

(28.1%)

11

(32.4%)

7

(23.3%)

6

(26.1%)

9

(28.1%)

42

(27.8%)

Media 1

(3.1%)

1

(2.9%)

1

(3.3%)

0

(0%)

1

(3.1%)

4

(2.6%)

Total 32

(100%)

34

(100%)

30

(100%)

23

(100%)

32

(100%)

151

(100%)

Rep. by political party Seminar 1 Seminar 2 Seminar 3 Seminar 4 Seminar 5

Participation Financing Political

patronage

Parties Elections Total

SDE 2

(22.2%)

3

(27.3%)

2

(22.2%)

1

(9.1%)

4

(33.3%)

12

(23.1%)

RE 1

(11.1%)

2

(18.2%)

2

(22.2%)

3

(27.3%)

1

(8.3%)

9

(17.3%)

IRL 0

(0%)

1

(9.1%)

0

(0%)

1

(9.1%)

1

(8.3%)

3

(5.8%)

KESK 3

(33.3%)

3

(27.3)

3

(33.3%)

3

(27.3%)

3

(25%)

15

(28.8%)

EER 1

(11.1%)

1

(9.1%)

0

(0%)

0

(0%)

0

(0%)

2

(3.8%)

ERKE 1

(11.1%)

1

(9.1%)

2

(22.2%)

2

(18.2%)

3

(25%)

9

(17.3%)

DEM 1

(11.1%)

0

(0%)

0

(0%)

0

(0%)

0

(0%)

1

(1.9%)

Total 9 11 9 11 12 52

Representation at the DDay Participants Population

1. Gender (n=299)

Male 45% 45%

Female 55% 55%

2. Age (n=294)

18-35 18% 34%

36-55 32% 34%

56+ 50% 32%

3. Education (n=299)

Elementary 4% 17%

Secondary 18% 27%

Vocational 26% 33%

Higher 52% 24%

4. Living place (n=296)

Town 64% 70%

Country 36% 30%

Transparency

• Guaranteed at most stages

- Transparent at the initial crowdsourcing and

the final Deliberation Day phase

- failed at the ideas systemisation and

analysis phase

• National public broadcasting company as media

partner

- local media less involved

• Mainly new technologies and social media

dependent: People’s Assembly portal, online

streamings, FB

Inclusion

• Aimed at all inclusiveness

• Random, targeted and self-selection

mechanisms used

• Politicians, experts (public officials,

representatives of civil society

organisations, academia), and the public

involved at all stages

- Yet, not equal at all stages

• Full representativeness not reached

Political outcomes • Out of the 15 presented ideas:

– Two fully implemented (regulation on popular

initiatives, lowering the no needed for

establishing a pol. party)

– Four partly implemented

– Three included in the Coalition Agreement of

the Government (assumed office March 2014)

Longer term impact:

- Vabaerakond established in 2014 with 650

members, 8 seats in the Parliament (2015)

- www.rahvaalgatus.ee (2016)

Some conclusions • Crowdsourcing suitable for addressing

highly salient societal issues:

– tensions smoothened by involving the

public;

- can have considerable political impact

• Various selection mechanisms needed to

improve representativeness (gender and

age)

• Cooperation betw. different societal

groups

[email protected]

Skype: neleleosk

Thank you!