Bibliographic record
Abstract
Over the past decade, radicalisation has emerged as perhaps the most pervasive framework for understanding micro-level transitions towards violence. However, the concept has not only become a dominant policing framework, but also an overarching governmental strategy encompassing surveillance, security, risk and community engagement. The emergence of this strategy has been accompanied by a whole host of analysts, advisers and scholars, who claim to possess ‘expert’ knowledge of individual transitions towards political violence. Revisiting ‘Radicalisation: the journey of a concept’, Arun Kundnani’s 2012 typology of such ‘expertise’ ( Race & Class, doi 10.1177/0306396812454984), the author comparatively examines scholarly developments in relation to ‘radicalisation’ and juxtaposes new knowledge claims with official government counter-radicalisation strategies and funding programmes in the UK, US and Canada to highlight how some of the most problematic knowledge claims continue to influence social policy as we move forward in the global ‘war on terror’.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.173 |
| Scholarly communication | 0.024 | 0.045 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".