“A solução para o oligopólio das publicações científicas é reinvestir nas plataformas nacionais que possuímos coletivamente”. Entrevista com Vincent Larivière
Bibliographic record
Abstract
Entrevista con Vincent Larivière, experto reconocido mundialmente en temas de acceso abierto, publicaciones científicas, multilingüismo y sociología de la ciencia. Doctor en Ciencias de la Información por la Universidad McGill, detenta la Cátedra UNESCO sobre la Ciencia Abierta y es co-titular de la Cátedra de investigación de Québec sobre la "descubrabilidad" de contenidos científicos en francés [Chaire de recherche du Québec sur la découvrabilité des contenus scientifiques en français]. También es profesor en la Escuela de Bibliotecología y Ciencias de la Información de la Universidad de Montréal, Director Científico del consorcio editorial Érudit, y vicedirector científico del Observatorio de Ciencia y Tecnología de la Universidad de Québec en Montréal. En esta entrevista, nos enfocamos en las publicaciones científicas, la evaluación y los indicadores de CyT, y las propuestas de la ciencia abierta.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".