Discursos d'odi a l'aula: Context i eines pedagògiques
Bibliographic record
Abstract
L'educació pot esdevenir una eina de doni suport als canvis de la societat i permetin a les persones donar resposta als desafiaments als que han de fer front. Cada moment social té els seus reptes i actualment l'ascens de discursos d'odi repercuteix de forma diària als nostres centres educatius. De forma explícita o implícita, a diari veiem com els discursos de l'alumnat es nodreixen de rumors, estigmatitzacions o intolerància cap a certs col·lectius, moltes vegades vulnerables. Sense voler ser una esmena a la totalitat de les pràctiques educatives tradicionals en favor de la tolerància, aquest article vol actualitzar conceptualment certs aspectes d'aquestes, mirant d'adaptar-les al context actual, tot donant eines als docents per tal de poder afinar les respostes a discursos cada vegada més simples però més captivadors per part del nostre alumnat.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.024 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| 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".