Solving Crimes Online: The Contribution of Citizens on the Reddit Bureau of Investigation
Bibliographic record
Abstract
Le développement des technologies numériques participe à l’émergence de nouveaux types d’initiatives citoyennes de sécurité. Cet article explore quatre formes de contribution en ligne de citoyens en matière de gouvernement du crime issues des sciences sociales et humaines : le travail de surveillance non institutionnalisée, le crowdsourced policing, les online vigilantes et la civilian police. Les enjeux soulevés par chacune de ces formes de contribution sont mobilisés pour mettre en lumière les résultats d’une démarche d’observation exploratoire au sein du Reddit Bureau of Investigation, un forum Internet anglophone qui regroupe des citoyens dont l’objectif est la résolution de crimes. Les résultats témoignent du rôle clé des technologies numériques et de surveillance dans l’émergence de ces pratiques, de nouvelles conceptions du rôle du citoyen en matière de gouvernement du crime, de la construction d’une identité commune en réaction à la figure du vigilante et du déploiement d’une expertise profane d’investigation.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".