Uma ciência coordenada? Revisitando tentativas de coordenar a pesquisa acadêmica
Bibliographic record
Abstract
A pandemia COVID-19 tem desafiado uma infinidade de atores e de setores a realizar ações coletivas para resolver a dramática crise global de saúde, revivendo um problema recorrente na história da humanidade: a coordenação das ações, ou a falta dela. A ciência não tem exceção: a concorrência desenfreada de países e empresas farmacêuticas para gerar vacinas e tratamentos revela que, longe de ser resolvida, a coordenação da pesquisa continua sendo uma questão válida. Este artigo faz uma revisão crítica das tentativas de coordenação da ciência dos últimos 40 anos, com particular interesse nos países federais, mais propensos à dispersão, duplicação e lacunas. Uma revisão da literatura permitiu uma revisão das políticas e instrumentos de coordenação e culminou com a proposta de estudar as redes de políticas em ciência, em busca de uma análise mais granular dos atores do ecossistema de pesquisa.
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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 | 0.076 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.045 | 0.042 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".