REPRESENTACIONES DE LAS MEDIDAS GUBERNAMENTALES EN EL CONTEXTO DEL COVID-19
Bibliographic record
Abstract
Se considera determinante para detener la rápida propagación del COVID-19, lograr adherencia de lapoblación a las políticas públicas preventivas indicativas de comportamiento. Las recomendaciones han sidodiferentes en los distintos países y han arrojado resultados variados en términos de prevalencia del COVID-19(Montreal Behavioural Medicine Centre, 2020). El Estudio International COVID- 19 Awareness and ResponsesEvaluation Study (ICARE) asociado a las políticas de salud pública busca conocer si es loable optimizar lasestrategias aplanando la curva de infección por COVID-19. Entre los hallazgos se relevó una preocupación centralen el 80 % de los participantes de que un miembro de la familia no conviviente o allegados se infectará de COVID19. Las respuestas exhibían mayor desazón por los impactos potenciales del COVID-19 en otras personas cercanasque en sí mismos. Estos hallazgos podrían dar cuenta de conductas de solidaridad y prosociales muy relevantes ala hora de diseñar campañas de prevención y desarrollo estratégico de políticas públicas, fomentando y haciendofoco en el cuidado del otro.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.030 | 0.031 |
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; both teacher heads agree on what is shown here.
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".