Leveraging digital public infrastructures for the common good to promote inclusive and sustainable economic development in Brazil
Bibliographic record
Abstract
The Government of Brazil is implementing an agenda of economic transformation that aims to bring economic, social and environmental priorities into alignment. Realising its full potential will require a parallel agenda of state transformation, to empower the public service with the policies, tools, institutions and capabilities needed to successfully direct growth and shape markets that work for the people of Brazil and for the planet. Thoughtful design and governance of Digital Public Infrastructures (DPI) are a critical part of this agenda. DPI — shared digital systems that are secure, interoperable, based on open standards and promote access to services for everyone — can be designed according to “common good” principles and oriented around policy priorities. This working paper explores the challenges and opportunities of leveraging DPI to support Brazil’s economic transformation, building on Brazil’s history of digital transformation initiatives. It looks at what this approach could mean for Brazil’s Rural Environmental Registry (CAR) as an illustrative case study. This working paper was used to inform a virtual workshop held remotely on September 20th, 2024, with representatives of 29 organizations. It has been updated to reflect the insights shared by participants. This working paper was written as part of a project funded by the Open Society Foundations, led by Professor Mariana Mazzucato (PI) with Professor David Eaves as Co-PI of the Digital Public Infrastructure workstream, as part of a partnership between the Institute for Innovation and Public Purpose (IIPP) and Brazil’s Ministry of Management and Innovation in Public Services (MGI).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".