Bibliographic record
Abstract
La question de la justice des systèmes d’éducation est au cœur des débats dans de nombreux pays. La justice est historique rattachée au concept d’égalité, mais que faut-il égaliser quand il est question d’éducation? La littérature à cet effet est abondante, mais n’est pas dénuée de tensions. À partir d’auteurs choisis, j’ai regroupé les différents termes à égaliser quand il est question de justice en éducation pour ensuite les relier à des approches philosophiques. Sans vouloir tomber dans le piège d’apposer des étiquettes souvent réductrices, cet article veut mettre en lumière les différentes considérations philosophiques principalement mobilisées dans les débats entourant la justice éducative. Au final, il s’agit d’une invitation à élargir le cadre de références quand il est question de la justice des systèmes éducatifs.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".