Dix petits hacks Tinder : les algorithmes au service d’une économie spéculative des rencontres amoureuses et sexuelles
Bibliographic record
Abstract
La médiation des rencontres amoureuses et sexuelles repose sur une longue tradition de services-conseils. Cet article analyse un corpus de guides-conseils développés autour de l’application populaire Tinder. Notre analyse suggère que la pratique de blackboxing algorithmique, soit le fait de caractériser les algorithmes à titre de boîtes noires, est centrale aux guides étudiés. Ces guides misent sur l’opacité présumée des algorithmes de Tinder pour vanter certains produits, conseils ou services visant la facilitation des rencontres. La pratique de blackboxing algorithmique renforce principalement l’injonction de hacker Tinder. Dans ce contexte, la notion de hack renvoie à l’importance pour les usagers de moduler leurs comportements afin de les rendre plus algorithmiquement intelligibles, notamment en fonction de leur genre. Ainsi, nous suggérons que les algorithmes de Tinder participent à reproduire une économie spéculative des rencontres amoureuses et sexuelles.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".