El caso Séralini y la confianza en el orden normativo dominante de la ciencia
Bibliographic record
Abstract
¿De dónde viene la confianza en la ciencia?; ¿cómo se nutre y se refuerza? En un contexto en el que, bajo el pretexto de la crisis en las finanzas públicas, las políticas científicas nacionales parecen favorecer el camino del financiamiento de la ciencia a través de asociaciones con el sector privado, en particular con las grandes empresas industriales,1 la confianza en la ciencia sigue siendo el argumento principal para justificar el mantenimiento e incluso reforzar el apoyo gubernamental a la investigación. El tema de la confianza parece, así pues, ser fundamentalmente político y no sólo ético: una ciencia que inspira confianza en los ciudadanos-contribuyentes podrá justificar su financiamiento con fondos públicos, a través de la riqueza colectiva.
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 | 0.048 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.085 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".