“Jeunes à risque”: Généalogie d'un langage problématique
Bibliographic record
Abstract
Se référant aux études sur la gouvernementalité, cet article propose une généalogie de la catégorie des “jeunes à risque” dans les recherches sur les adolescents et les jeunes adultes. La société du risque, les lieux de diffusion des savoirs scientifiques, les axes prioritaires du financement public et les structures institutionnelles encadrant la pratique des chercheurs forment une conjoncture singulière où a pu s'élaborer un véritable langage des “jeunes à risque.” Fruit d'une démarche de réflexivité critique, l'analyse souligne la responsabilité des chercheurs dans la légitimation de ce langage problématique et les défis que comporte le projet de le transformer. Based on governmentality studies, this article sketches out a genealogy of the “youth at‐risk” category in contemporary youth studies. A convergence of risk society, sites of dissemination for youth research, state priorities in research funding, and institutional structures that frame research practices serve to create a unique historical moment for the emergence of regulatory “youth at‐risk” language. As part of a critical reflexive approach to youth research, this article underscores youth scholars' responsibility to challenge this problematic language and pins down some of the difficulties this project faces in the academy.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".