Políticas Públicas de Informação e os atores informacionais envolvidos em tentativas de censura de livros no Brasil (2020-2025):
Bibliographic record
Abstract
A censura a livros no Brasil, historicamente associada a regimes autoritários, tem assumido novas formas no Brasil, no período recente (2020–2025), marcada por disputas políticas, religiosas e morais, com ênfase em obras sobre gênero, raça e memória política. Este estudo analisa os atores políticos e informacionais envolvidos nesses processos, a partir do conceito de regime de informação e do framework COQ3 (Secchi, 2008). A pesquisa, de abordagem qualitativa e natureza exploratória, utilizou revisão bibliográfica, análise documental e estudo de casos de censura às obras O Avesso da Pele e Capitães da Areia, mapeando quatro grupos de atores: sociais, regulatórios, profissionais e econômicos. Os resultados revelam tensões entre restrição e resistência, evidenciando a necessidade de políticas públicas que assegurem diversidade e liberdade intelectual. O estudo procura contribuir para compreensão da censura literária como problema público de informação e destaca a importância da articulação entre Estado e sociedade civil no enfrentamento desse fenômeno.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".