Paraguay

Datathon 2020

Datathon 2020 was a hackathon organized by Civilab with the support of several organizations, including the National Innovation Strategy (span. ENI) and other civil and private organizations such as Wendá, Tomate Estudio and Cervepar. The objective was to promote the culture of open data as a tool to improve government transparency. The Datathon took place in September 2020, with 4 days of talks with 10 specialists, and then 3 days of hackathon to put into practice the knowledge acquired using the open data of the Paraguayan government. One of the proposals developed during this hackathon was the analysis of parliamentary data.

Institutional design

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Formalization: is the innovation embedded in the constitution or legislation, in an administrative act, or not formalized at all?

Frequency: how often does the innovation take place: only once, sporadically, or is it permanent or regular?

Mode of Selection of Participants: is the innovation open to all participants, access is restricted to some kind of condition, or both methods apply?

Type of participants: those who participate are individual citizens, civil society organizations, private stakeholders or a combination of those?

Decisiveness: does the innovation takes binding, non-binding or no decision at all?

Co-governance: is there involvement of the government in the process or not?

Formalization
not backed by constitution nor legislation, nor by any governmental policy or program 
Frequency
single
Mode of selection of participants
open 
Type of participants
citizens  
Decisiveness
democratic innovation yields no decision  
Co-Governance
no 

Means


  • Deliberation
  • Direct Voting
  • E-Participation
  • Citizen Representation

Ends


  • Accountability
  • Responsiveness
  • Rule of Law
  • Political Inclusion
  • Social Equality

Policy cycle

Agenda setting
Formulation and decision-making
Implementation
Policy Evaluation

Sources

How to quote

Do you want to use the data from this website? Here’s how to cite:

Pogrebinschi, Thamy. (2017). LATINNO Dataset. Berlin: WZB.

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