Containing the Narrative: Strategy and Tactics in Countering the Storyline of Global Jihad
Bibliographic record
Abstract
It has long been recognised that telling a better story is an important part of countering the appeal of Global Jihad. The ‘War on Terror’ will be difficult to win if the ‘War on Ideas’ is lost. The mushrooming literature on terrorism notwithstanding, the counter‐narrative issue has been the subject of surprisingly scant academic attention. Part of the problem is that this is an issue with relatively little empirical work. Still, significant inferences for a counter‐narrative strategy can be drawn from existing research. Here we argue that counter‐narratives must be tailored to different audiences and must be designed to attack particular mechanisms of radicalisation. In contrast to the top‐down approach that has thus far been advocated to confront the claims of Global Jihad ‘head on’, what is actually needed is a bottom‐up approach that reaches vulnerable individuals early on by means of a nuanced approach that is sensitive to the multiple logics of radicalisation.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".