¿Qué se necesita para que la promoción de la salud tome en serio al racismo estructural?
Bibliographic record
Abstract
El creciente aumento de una conciencia pública sobre la raza -a raíz del asesinato de George Floyd, un hombre negro, cometido por el oficial de policía blanco Derek Chauvin en Mineápolis (Estados Unidos) el 25 de mayo del 2020 -, luego acentuado por las incesantes voces del movimiento Black Lives Matter (Las Vidas Negras Importan), de numerosos activistas, de comunidades e intelectuales, puede ser algo que nunca se había visto.En este periodo de revisión de cuentas (especialmente en el norte del planeta), que refleja siglos de opresión y explotación racial al tiempo que se ve una resistencia de los pueblos racializados, ¿qué se necesita para que el campo de la promoción de la salud tome en serio al racismo?
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".