Nos savoirs, notre milieu de vie: Le savoir d'usage des locataires HLM familles
Bibliographic record
Abstract
Le propos du present ouvrage s’inscrit dans un champ de recherche aujourd’hui largement etaye qui met de l’avant le cercle vertueux de la valorisation des savoirs des usagers dans les differents axes d’intervention des politiques publiques. Nous assistons de ce fait a un vaste mouvement de remise en cause de la hierarchie traditionnelle des savoirs qui tend a valoriser, c’est-a-dire a reconnaitre, la plus-value de la prise en compte de l’experience des citoyens «?ordinaires?» dans les processus de prise de decisions qui les affectent directement. A partir de temoignages de locataires, de resultats de recherches participatives menees dans des habitations a loyer modique (HLM) au Quebec et de mises en perspective historiques et thematiques, ce livre met en evidence les savoirs d’usage bases sur l’experience dont sont porteurs les residents des HLM familles au Quebec. Il montre les processus d’apprentissage et de mobilisation de ces connaissances par les locataires et la maniere dont elles sont valorisees par les institutions, en particulier les offices. Il veut faire comprendre comment la prise en compte de ces savoirs peut contribuer a l’amelioration des conditions de vie et au developpement de la capacite d’agir des locataires dans le milieu HLM.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".