A comparison of two structured professional judgment tools for violent extremism and their relevance in the French context
Bibliographic record
Abstract
France has repeatedly been hit by terrorist attacks making the use of a structured professional judgement tool essential. Two such tools currently are used in Europe: VERA (Canada) and ERG 22+ (England and Wales). We compare these tools to assess which one would better suit the French context. We find that they have a lot in common in terms of their general content and intrinsic value. However, VERA’s main understanding of terrorism is ideology, whereas ERG’s perceives it as being essentially about identity. ERG was the first tool to introduce a measure for psychopathology; only VERA contains a list of protective factors. The two instruments were developed in similar ways with, for ERG, more emphasis on extremist offenders’ casework. Crucially, ERG 22+ has been developed on the basis of UK cases that are closer to the French extremist populations, with a legal threshold for what constitutes a terrorist or an assimilated terrorist offence, which is similarly low. Lastly, ERG’s structure is simpler and contains less items requiring access to classified data, a crucial factor in a jurisdiction with little interagency information sharing. Although the two tools are intrinsically comparable, ERG seems overall better suited to the French context. This exploratory conclusion ought to be confirmed with field comparisons.
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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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".