Comment désigner l’ennemi public international ? Pour une histoire conceptuelle de l’antiterrorisme onusien
Bibliographic record
Abstract
Comment l’ONU désigne-t-elle le « terrorisme » ? Partant du constat de l’ambiguïté et du caractère politique du concept de terrorisme, ce texte retrace l’histoire des débats onusiens entourant la façon de fixer la notion, en suivant les méthodes de l’histoire conceptuelle (Begriffsgeschichte). Devant les difficultés posées par la poursuite d’une définition universelle, la pratique du listage émerge au tournant du XXIe siècle, ce qui n’est pas sans conséquence sur le sens du concept et sa portée concrète. Avec le listage, c’est un antiterrorisme plus unilatéral, policier et décentralisé qui prend forme, aux dépens de la version juridique et multilatérale induite auparavant par l’idée d’une définition générale du terrorisme. Par le biais de cette histoire, il s’agit d’éprouver les contours mouvants de l’antiterrorisme onusien, et de poser les jalons de ce en quoi pourrait consister l’histoire des concepts pénaux internationaux.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".