“Don’t LAI to me”: a thematic analysis of Brazil’s first newsletter focusing on access to information
Bibliographic record
Abstract
Access to information (ATI) policies and legislations emerged globally from the need to enhance democratic systems, by allowing citizens to monitor political decisions and contribute to social change. However, ATI does not always play out in reality as it’s spelled out on paper. Brazil is one of the many countries that suffers from flawed access to information laws, with not-so-transparent documents being an all-too-common experience for requesters. \nFiquem Sabendo is an independent data agency that fights to hold the Brazilian ATI system to account. It is an organization committed to public transparency and, since 2019, has published 158 issues of the newsletter “Don’t LAI to me”, which includes “unpublished databases, news, tips and reports produced on or based on data obtained via ATI.” \nThrough a detailed a thematic analysis of 147 issues of the newsletter, this research project sought to observe whether it fulfills the journalistic role of “the watchdog,” and what other theoretical roles of journalism are expressed in its content. The research also examines the tools the newsletter provides citizens to help others access public resources and information in an autonomous way. One research goal is to answer the question of how the newsletter mobilizes key concepts in access to information legislation, like “human rights,” “transparency,” and “objectivity.” Overall, the driving goal of this research is to increase awareness about the LAI in Brazil and to add to discussions in the field, highlighting barriers and opportunities for improvement in information dissemination through the lens of journalism.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".