Normativities and judicial uses of technologies : the controversed illustration of neuroimaging in France and Canada
Bibliographic record
Abstract
L’observation du système nerveux, de son métabolisme et de certaines de ses structures est possible grâce à la neuroimagerie. Une littérature importante issue du « neurodroit » véhicule des imaginaires et des fantasmes relatifs aux possibilités judiciaires qu’offriraient ces technologies.Qu’il s’agisse de détection du mensonge, d’identification cérébrale des individus dangereux ou encore de prédiction de comportements déviants, la neuroimagerie, en l’état actuel des technologies, ne peut pourtant être sérieusement conçue comme pouvant faire l’objet de telles applications.L’utilisation de la neuroimagerie dans le cadre d’expertises est néanmoins une réalité, dans les tribunaux canadiens comme dans la loi française.Cette thèse souligne que les conceptions des technologies dont témoignent les deux systèmes juridiques étudiés s’avèrent lacunaires, ce qui engendre des risques. Elle évoque les conditions du recours à une normativité extra-juridique, la normalisation technique, qui pourrait s’élaborer dans ce contexte controversé, et esquisse les traits d’un dialogue amélioré entre les normativités juridique et technologique.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".