L’industrie du kidnapping au Nigeria : une mise en perspective historique
Bibliographic record
Abstract
Ce commentaire décrypte les inquiétudes et les analyses que suscite l’industrie du kidnapping au Nigeria. Dans un premier temps, il montre les limites de recherches académiques parfois trop sensibles à la dramatisation des médias relativement à un phénomène qui est présenté comme inédit. Sur le plan méthodologique, l’article met également en lumière des problèmes de définition et de mesure statistique. Les confusions entretenues à propos des kidnappings soulignent alors la nécessité d’une perspective historique sur l’esclavage. Des récurrences apparaissent lorsqu’on s’intéresse aux logiques économiques des ravisseurs et aux narratifs discriminants à l’égard des étrangers. Étant donné leur ampleur et leur impact, les kidnappings d’aujourd’hui ne sont cependant pas comparables à la traite et il serait bien hasardeux de soutenir qu’ils seraient plus ou moins criminels ou politiques.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".