Bibliographic record
Abstract
Transgresser, c’est franchir une limite, une norme, sociale, scolaire, familiale, soit une pluralité de normes et parfois des conflits, par exemple entre nature et culture(s), école et famille. Alors, transgresser, est-ce un moment de l’apprentissage, ou une mise en question, ou encore une condition de celui-ci ? La transgression est l’acte d’un sujet parlant, apprenant : je transgresse pour apprendre, expérimenter, comprendre… soit un projet ; ou je transgresse pour me protéger, me conformer à mon milieu, soit un refus. L’exemple des enfants plurilingues montre comment la transgression d’une norme ou une autre peut être une prise de conscience et un révélateur. Transgression in the learnings: Uses and limits Abstract: To transgress is to go beyond a limit, a rule that can either be social, educational or familial. There is a plurality of norms and sometimes conflicts, for example between nature and culture(s), school and family. Thus is to transgress, a moment of learning, a questioning or a condition of the latter? Transgressing is an act of a speaking learning subject: I transgress to learn experiment and understand. Thus, it is a project. Or I transgress to protect myself, to comply with my environment: then, it is a refusal. The example of multilingual children shows how the transgression of a norm or that of another one can serve as an awareness or an indicator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".