Bibliographic record
Abstract
Twitter constitue un terrain de jeu de prédilection pour le groupe État islamique (EI) : espace de médiatisation, de revendication et de mobilisation. Depuis sa matrice de naissance afghane jusqu’à ses incarnations contemporaines, le mouvement jihadiste transnational (MJT) est devenu, au cours des deux dernières décennies, une mouvance globalisée sans pareille. Au cœur de cette mutation se trouve un outil, le web, qui n’a cessé d’être apprivoisé et investi par les acteurs de la mouvance jihadiste. Dans cet article, Benjamin Ducol, chercheur post-doctorant au Centre International de Criminologie Comparée (CICC) de l’Université de Montréal, offre une genèse historique de l’adaptation du MJT au champ numérique. À travers cette perspective historique, il dresse un portrait des ramifications numériques successives du MJT, et tente d’en évaluer les grandes tendances actuelles. En contrechamp, l’auteur offre également un bref panorama des ripostes orchestrées face à ce phénomène, qu’elles émanent des États, des acteurs privés ou de la société civile.
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 | 0.008 |
| 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 | 0.011 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".