En homenaje a las contribuciones de Paul R. Pintrich a la investigación sobre Psicología y Educación
Bibliographic record
Abstract
Esta parte del monográfico presenta los comentarios de varios especialistas internacionales sobre las contribuciones de Paul R. Pintrich a la investigación sobre Psicología y Educación. Lucia Mason (Universidad de Padua, Italia) comenta las aportaciones del trabajo del profesor Pintrich y sus colaboradores sobre las creencias epistemológicas y su papel en el aprendizaje y la enseñanza. Gale Sinatra (Universidad de Nevada, USA) y Margarita Limón (Universidad Autónoma de Madrid, España) respectivamente, ofrecen su visión de las contribuciones de Paul a la investigación sobre cambio conceptual. Philip Winne (Simon Fraser University, Canadá) por un lado, e Ignacio Montero y Mª José de Dios (Universidad Autónoma de Madrid, España), por otro, analizan las aportaciones tanto teóricas como empíricas de Paul Pintrich en el ámbito del aprendizaje autorregulado y la motivación en contextos académicos.Finalmente, Richard E. Mayer (Universidad de California en Santa Bárbara, USA), Patricia A. Alexander (Universidad de Maryland, USA) y Erik De Corte (Universidad de Lovaina, Bélgica) presentan un comentario general sobre las contribuciones y repercusiones de la obra de Paul Pintrich en Psicología y Educación.
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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 | 0.009 | 0.021 |
| 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 | 0.014 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".