9. ¿Qué impacto tiene un aumento de las tasas de matrícula en el acceso a la universidad? El ejemplo de Quebec
Bibliographic record
Abstract
En varios paises los estudiantes deben abonar tasas para asistir a la universidad. Algunos analisis sobre la evolucion del acceso a la universidad y la de las tasas de matricula han demostrado que estas tasas no influyen en el acceso, lo que justifica la decision de incrementarlas para aumentar la financiacion de la universidad. En este articulo criticamos estos estudios, recordando diversas precauciones metodologicas a menudo ignoradas por sus autores. Llevamos a cabo un analisis longitudinal e historico de la situacion existente en Quebec y Ontario. Encontramos un efecto negativo de las tasas de matricula en el acceso a la educacion universitaria. Este efecto negativo es mas pronunciado entre los alumnos cuyos progenitores presentan menores niveles educativos, entre los francofonos de ambas provincias y entre los estudiantes de mas edad.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".