La regulación de la escritura académica en el doctorado: el impacto de la revisión colaborativa en los textos
Bibliographic record
Abstract
La investigación pretende identificar las dificultades de los estudiantes de doctorado cuando escriben su trabajo de investigación y analizar las estrategia de revisión colaborativa que son capaces de utilizar frente a esas dificultades. Participaron seis estudiantes de doctorado que revisaron en parejas tres versiones de sus repectivos proyectos de tesis. Se analizó el discurso de cada pareja en las sesiones de revisión (18 horas) y los cambios introducidos en los textos (18 borradores). Los resultados indican que los problemas para conectar la información son los más frecuentes. El análisis detallado de los datos indica que en ocasiones la intencionalidad comunicativa no se corresponde con el tipo de recurso utilizado y que las estrategias de revisión sólo son eficaces si los estudiantes pueden difinir adecuadamente los problemas del texto. Se comentan las implicaciones educativas de estos resultados.
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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 | 0.068 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".