Bibliographic record
Abstract
Les programmes de villagisation forcée mis en place durant la guerre froide globale permettent d’explorer le processus de « miliciarisation forcée » de la population civile regroupée dans ces villages. Si l’enrôlement de personnes déplacées dans des milices semble avoir été une constante dans le cadre de ces programmes, comment la villagisation forcée est-elle devenue tout à la fois un outil pour combattre la « menace communiste », un programme de « développement forcé » et un dispositif destiné à forcer la miliciarisation de la population déplacée ? À partir de l’analyse de plusieurs cas en Amérique latine, et notamment celui de l’Argentine, nous voulons comprendre pourquoi le dispositif milicien n’a pas toujours eu le même degré de systématicité ni le même degré de confiance des armées locales vis-à-vis de la population à miliciariser. Cet article se veut avant tout une contribution critique à l’étude de l’appropriation locale d’une technique de contre-insurrection et de développement forcé ainsi qu’une réflexion sur les effets à long terme sur la population ayant subi un processus de miliciarisation forcée.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".