Les milieux alternatifs de scolarisation : de nouveaux entrepreneurs sociaux ?
Bibliographic record
Abstract
L’entrepreneuriat social a le vent en poupe au Québec. Prisé par les politiques néolibérales contemporaines, on retrouve désormais cette expression aussi bien dans les milieux éducatifs qu’associatifs (Claude et Gaudet, 2018). Avant de s’intéresser à ses implications, il importe de comprendre les différents récits et réalités que cette expression recouvre. Sur la base de trois études de cas au sein d’une école de la rue, d’une école privée alternative et d’une association de raccrochage scolaire, cet article montre comment les différentes logiques sociales mises en place par certains milieux alternatifs de scolarisation du Québec (MAS) s’arriment à des logiques marchandes. Le cadre des économies de la grandeur (Boltanski et Thévenot, 1991) fait ressortir une pluralité de logiques qui se répartissent sur un continuum. Ces logiques se co-construisent, s’articulent, voire parfois s’opposent, afin de rendre compte des multiples facettes de l’entrepreneuriat social.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Case studies of alternative schooling settings as social entrepreneurship; the object is education provision.
The study examines social entrepreneurship and alternative schooling in Quebec.
Social entrepreneurship framing of alternative Quebec schooling; education/society, not research systems.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".