Brazil

Dear Diary

Querido Diario (lit. Dear Diary) is a project for better control and accountability in local public management through a digital platform for the evaluation and production of open data. The project is supported by Open Knowledge Brazil and by Empatía, a Latin American network to promote the use of artificial intelligence. The project aims to implement artificial intelligence and machine learning to facilitate access to public management data for all the over 5,000 Brazilian municipalities. Citizens can participate by submitting data and registering their municipalities in the database to feed artificial intelligence, still under development.

Institutional design

?

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
regular
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.

Would you like to contribute to our database?

Send us a case