Bibliographic record
Abstract
Il convient d'établir une distinction importante entre les mesures formelles et informelles de profilage des risques. Selon la plupart des acteurs, les mesures formelles en la matière, lesquelles s'inspirent de données statistiques, représentent la meilleure facüon de prédire et, partant, d'identifier des personnes ou des situations présentant un niveau élevé de risque. Cependant, il faut reconnaître que les profils formels sont fondés en grande partie sur des profils informels établis à partir des expériences et des cultures de travail de fonctionnaires oeuvrant dans le domaine de la sécurité. Il s'ensuit que les profils formels ont tendance à renfermer les mêmes erreurs et à exprimer des prophétie auto-réalisatrice qui engendrant des erreurs de fait ainsi que des conséquences discriminatoires pour les cas faux-positifs. Ceci dit, tous les profils accusent une multitude de problèmes. Par exemple, — et il s'agit du problème qui est sans doute le plus évident et le plus important —, les personnes faisant l'objet d'interdictions peuvent repérer facilement les mesures de profilage et modifier leurs opérations en conséquence en y intégrant des activités ou des individus qui ne correspondent pas aux profils établis. De là à conclure que les mesures de sécurité fondées entièrement sur l'analyse du risque pourront devenir moins efficaces que certaines solutions de rechange, telles que les interdictions ciblées aléatoirement. A key distinction needs to be made between formal and informal risk profiling. It is usually assumed that formal risk profiling, based on statistical data, provides an optimal means of prediction and thus of detecting high-risk individuals or situations. However, it must be recognized that formal profiles are based extensively on informal profiles — those built upon the experience and working cultures of security-related officials. Formal profiles are thus prone to the same errors and tend to produce self-fulfilling prophecies, resulting in both errors of fact and discriminatory consequences for falsepositive cases. All profiles, however, are fraught with problems, perhaps the most obvious and significant being that the targets of interdiction readily become aware of them and modify their operations to incorporate activities and/or individuals that do not fit existing profiles. Consequently, totally risk-based security, ironically, may become less effective than alternatives such as randomly assigned focused interdictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".