“La solución al oligopolio de publicaciones científicas es reinvertir en plataformas nacionales que nos pertenezcan colectivamente”
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.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".