La prédiction de l’homosexualité à l’ère de l’intelligence artificielle : une analyse de trois controverses technoscientifiques
Bibliographic record
Abstract
Contexte : Les plateformes numériques participent à une reconfiguration des imaginaires liés à la prédiction de l’homosexualité. Analyse : Cet article analyse trois controverses technoscientifiques. La première aborde la prédiction de l’orientation sexuelle des usagers et usagères Facebook sur la base de contenus aimés. La seconde traite d’un dispositif de reconnaissance faciale visant à prédire l’homosexualité. Enfin, la troisième porte sur la capacité présumée des algorithmes de TikTok à influencer l’orientation sexuelle de ses membres. Conclusions et implications : L’analyse des différents imaginaires liés à la prédiction permet de saisir les préoccupations de divers acteurs sociaux quant aux implications que l’intelligence artificielle soulève pour les communautés LGBTQ+.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".