Bibliographic record
Abstract
Comment punit-on au Burkina Faso, au Bénin, au Japon, en Russie, en France, en Algérie, en Angleterre, aux États-Unis ou encore au Québec ? Ce numéro 81 de la Revue internationale d'éducation de Sèvres souligne les questions et les défis que neuf systèmes scolaires très différents ont à relever. Il montre des écoles inquiètes. Mais il met aussi en lumière les solutions et les expériences originales qui sont mises en oeuvre. Aussi paradoxal que cela puisse paraître, la sanction est un analyseur de première importance pour comprendre ce qu’éduquer veut dire, à un moment donné, dans une société donnée. What do school sanctions entail in Burkina Faso, Benin, Japan, Russia, France, Algeria, England, the United States, or Quebec? This issue of the Revue internationale d'éducation de Sèvres highlights the issues and challenges that nine very different school systems face. It shows growing concern in schools. But it also highlights the original solutions and experiences that are being implemented. As paradoxical as it may seem, sanction is a primary means of analysis for understanding what education means, at a given moment, in a given society. ¿Cómo se castiga en Burkina Faso, en el Benín, en Japón, en Rusia, en Francia, en Argelia, en Inglaterra, en Estados Unidos o en Quebec? Este número 81 de la Revue internationale d'éducation de Sèvres subraya las cuestiones y los retos que nueve sistemas escolares muy distintos tienen que resolver. Muestra unas escuelas inquietas pero pone asimismo en evidencia las soluciones y las experiencias originales desarrolladas. Por muy paradójico que esto parezca, la sanción resulta ser un elemento de análisis de primera importancia para entender lo que educar quiere decir, en un momento dado y en una sociedad dada.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 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".