Classes populaires et engagement militaire : des affinités électives aux stratégies d’insertion professionnelle
Bibliographic record
Abstract
Fondé sur une série d’observations réalisées au sein de trois centres de recrutement militaire (CIRFA de Saint-Denis, Lyon et Marseille), cet article explore la question de l’engagement militaire de jeunes ayant pour dénominateur commun d’être issus de milieux sociaux populaires. Cet article met en perspective les trajectoires sociales d’individus issus de ces segments de population et les spécificités des processus de recrutement en vigueur dans le monde militaire. Il s’agit plus précisément de décrypter l’articulation entre les parcours biographiques des enquêtés et leurs décisions d’engagement. Loin de présenter ces acteurs comme des agents passifs ou attentistes, l’étude met en exergue les multiples ressources et les diverses stratégies que ces populations déploient afin d’intégrer une institution éminemment emblématique.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".