Libro de Memorias. XXIII y XXIV Congreso Nacional de Ciencia, Tecnología y Sociedad - XIII Festival Internacional de Matemáticas
Bibliographic record
Abstract
En el 2021 organizamos el XXIII CONCITES, Congreso Nacional de Ciencia, Tecnología y Sociedad con gran participación nacional e internacional, en formato híbrido, con un evento virtual del 17 al 21 de agosto. Siguió luego un programa de extensión el sábado 28 de agosto, que fue dedicado a talleres, laboratorios y giras presenciales, en el Colegio de Señoritas en San José, Centro universitario UNED en Alajuela, la Sede UISIL en San Isidro del General y el Centro universitario UNED en Ciudad Neilly. En el 2022 realizamos dos grandes congresos, el XXIV CONCITES: Congreso Nacional de Ciencia, Tecnología y Sociedad y el XIII FIMAT: Festival Internacional de Matemáticas, en dos formatos, una parte virtual del 30 de agosto al 1 de setiembre. y luego un evento presencial, el 2 y 3 de setiembre en la Universidad Internacional San Isidro Labrador, UISIL, en Pérez Zeledón.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.221 | 0.144 |
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".