Investigadoras jóvenes: ¿Son tus tiempos competitivos para el sistema de financiamiento público?
Bibliographic record
Abstract
El objetivo de esta investigación es identificar las características de los y las jóvenes graduados que aplican a fondos públicos de inicio a la investigación científica en las distintas áreas del conocimiento y los factores que afectan su asignación. Se utilizaron estadísticas públicas, aplicándoles métodos de análisis de conglomeración jerárquico y el modelo Heckman. Se identificaron tres perfiles: dos de investigadores/as (54,3%) con y sin beca doctoral que logran insertarse en el sistema, y un tercer perfil (45,7%) con beca doctoral que no se inserta. La totalidad de dichos perfiles presenta brechas por sexo. Las mujeres que se insertan en el sistema de financiamiento demoran, en promedio, casi tres años, y el mayor tiempo en obtener su grado doctoral influye negativamente en su inserción. Se concluye que el desarrollo científico de hombres y mujeres tiene distintos tiempos, hecho que debiera considerarse en futuros diseños de políticas si el modelo competitivo continúa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".