Using Information Science to enhance educational preventing violent extremism programmes
Bibliographic record
Abstract
Educational preventing violent extremism (EPVE) programs have had (to date) little if any theoretical underpinning. Given their proliferation in jurisdictions such as Canada, Australia, the United Kingdom, and other European countries, such an absence is notable but not unexpected given the political sensitivities attached to them. These programs remain an emerging policy area which is still “finding its feet,” around which their legitimacy and efficacy is keenly debated. This paper argues for adopting theoretical principles drawn from information science research based upon information behavior models to provide a framework for the design and development of such programs and against which their efficacy can be tested. We demonstrate how this approach can be applied through thematic analysis of the theory of change models of EPVE programs implemented in England and Wales, designed to increase awareness and understanding of radicalization among young people, their carers, and professionals. This article is ground breaking and of international significance, being the first to apply learning from information science to practice in furthering policy goals around countering radicalization and extremism in the United Kingdom and other jurisdictions.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".