Contester les algorithmes sur le terrain électoral : le cas des machines à voter en France
Bibliographic record
Abstract
Depuis l’agora athénienne, les sociétés politiques n’ont eu de cesse d’améliorer les protocoles techniques qui régissent la compétition électorale. Culture du numérique aidant, ce souci de performance mène progressivement à l’informatisation du geste électoral. En France, c’est en 2003 que les machines à voter ont fait leur entrée dans les bureaux de vote. Réponse technique à une injonction de rapidité et de rentabilité, l’urne électronique enregistre, comptabilise et dépouille les voix. Toutefois, l’avènement de dispositifs électroniques dans cet espace tant sanctuarisé a suscité le développement de mouvements contestataires anti-vote électronique. Or les machines à voter reposent non pas sur une organisation collective (ce qui est le cas pour le vote papier) mais sur une programmation algorithmique. Dès lors, les sphères de contestation se voient inévitablement renouvelées.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".