Insider perspectives: insights from formers on their role as subject-participants in P/CVE research
Bibliographic record
Abstract
This article examines the contributions of ‘formers’, individuals who were previously affiliated with groups advocating violent extremism but who now work as researchers and practitioners in the P/CVE field. There are a limited number of studies assessing the value added of formers to P/CVE programs. We make two significant contributions to the body of work. First, we examine how formers can contribute not only to P/CVE practice, but to academic research of terrorism, and how their insights and experience can be employed for improving research designs and interview completion rates and responses. Second, none of the extant works incorporates the views of formers themselves in the assessments. Four of the co-authors of this article are formers who publish peer-reviewed and policy institute research, and they present their perspectives on who counts as a former, and how their individual backgrounds inform and bolster their research and praxis. We encourage researchers of political violence to emulate other fields of social science and incorporate appropriately trained formers as subject-participants to improve research in the field.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.023 | 0.037 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| 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".