Du non-profit au for profit : vers un « floutage » de la notion de public
Bibliographic record
Abstract
Maillé par une grande diversité d’associations communautaires à but non lucratif (non-profit), le quartier du South Bronx à New York est aussi le lieu d’accueil d’une grande partie des foyers (shelters) pour la population la plus pauvre de la ville. À partir d’une ethnographie auprès d’une association communautaire du South Bronx — le Community Association of the South Bronx (CASB) — l’article décrit la façon dont les organisationsnon-profitse sont transformées, s’adaptant au double processus de démantèlement de l’État social américain et à la délégation de la gestion des aides sociales aux villes. L’article interroge ainsi les rapports entre associationsnon-profitetfor profitet l’hybridation (Duvoux, 2015) qu’opère, dans les quartiers populaires, le rapprochement des deux logiques. Cette dynamique, facilitée par les mesures dérégulatrices en faveur du secteur privé, accompagne uneinformalisationde l’État, ici dans sa composante municipale. En partant de l’expérience de Mickey, un ancien prisonnier vivant dans le South Bronx, l’article retrace les effets en termes de criminalisation et de dépendances que crée l’imposition d’une logique de marché dans la gestion sociale desnon-profits.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".