Bibliographic record
Abstract
Les enjeux liés aux auteurs d’actes d’extrémisme violent ont favorisé ces dernières années l’essor de bon nombre de politiques publiques dans un contexte international marqué par la prégnance des atteintes à la sécurité. Au Cameroun, l’institutionnalisation des sorties de Boko Haram centrée sur le programme de désarmement, de démobilisation et de réintégration (DDR), lancé à la suite des redditions en 2018, illustre le passage d’une approche répressive à une approche préventive pour contrer l’extrémisme violent. À partir d’une enquête qualitative, cet article retrace les évolutions paradigmatiques du programme de DDR en tant que dispositif de prévention au niveau tertiaire, s’adressant spécifiquement aux ex-combattants désarmés et démobilisés. Il analyse ses effets sur la reconfiguration des acteurs mobilisés, en particulier les mères des ex-combattants, dont le rôle dans son déploiement s’avère déterminant, et les instances judiciaires, que le programme de DDR marginalise.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".