Framing local collaboration among frontline workers in the field of preventing violent extremism
Bibliographic record
Abstract
Local collaboration among frontline workers is probably one of the main challenges for the PVE field. Many Western countries working in this field have developed multi-agency models to address this problem and there is a growing interest in the literature on these challenges. However, few efforts have attempted to analyze these issues theoretically and empirically. Thus, this article aims not only to provide empirical information from practitioners' field experience on these issues but also to provide a comprehensive analytical framework for interpreting them. To this end, frame analysis theory has been used to describe and understand the challenges of collaboration in this field. A total of 90 participants from 64 organizations in 27 countries were interviewed for this study. Personal ties, professional alignment, conflicts of interest, law enforcement involvement, governmental framework, competition, and time invested in collaborative efforts were highlighted as the main factors explaining the success or failure of local collaboration.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| 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".