Bibliographic record
Abstract
Les destructions de biens culturels en temps de conflit armé ont généré de nombreuses dynamiques de politisation et de dépolitisation étudiées dans différents contextes (États, organisations internationales, etc.). Cet article propose d’explorer un aspect peu étudié par les travaux en science politique : la politisation des destructions par leurs auteurs. Il s’agit donc de rendre compte des mécanismes de politisation des destructions de biens culturels par le groupe État islamique en Iraq et en Syrie entre 2014 et 2019. Au-delà de l’application de la doctrine religieuse salafiste, la destruction organisée des biens culturels permet au groupe de se construire et de s’affirmer comme autorité politique en accaparant différents secteurs traditionnellement considérés comme de l’ordre du régalien (que ce soit la capacité d’édicter les normes sociales ou celle de les faire respecter – la police, l’armée, la justice et le domaine fiscal). De manière complémentaire à ces aspects formels, les destructions de patrimoine témoignent de la volonté de l’EI de construire sa légitimité afin de consolider ses revendications à incarner l’autorité politique.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".