Nota de les editores: Recerca actual sobre l’ensenyament de la gramàtica. Per a què serveix ensenyar gramàtica?
Bibliographic record
Abstract
La recerca sobre l’ensenyament de la gramàtica abasta temes diversos i adopta perspectives plurals. El III Congrés Internacional sobre Ensenyament de la Gramàtica (Congram19), celebrat a la Universitat Autònoma de Barcelona del 23 al 25 de gener de 2019, n’és una mostra. La presència de treballs de recerca actuals realitzats en diversos contextos va ser una oportunitat per a contribuir a un camp comú en el qual sigui possible reflexionar i debatre sobre les particularitats de la recerca realitzada en cadascun d’aquests contextos, lligada clarament a les finalitats assignades a l’ensenyament de la gramàtica. En aquest número especial es recullen les aportacions de 16 investigadors resultants d’aquest congrés. El lector trobarà aquestes contribucions en dues parts: la primera part, en el número anterior de Bellaterra Journal of Teaching & Learning Language & Literature (13.2), i la segona part, en el número present (13.3).
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".