Sortir de l’ombre, défier la peur : opportunités et défis des technologies numériques pour les minorités sexuelles et de genre au Cameroun
Bibliographic record
Abstract
Cet article analyse la portée politique des différents usages des technologies numériques que les minorités sexuelles exploitent à des fins de visibilité au Cameroun. Il s’appuie sur une enquête de terrain menée à Yaoundé et à Douala, entre 2017 et 2018, ainsi que sur une veille ethnographique numérique réalisée sur Instagram, Facebook, Twitter et TikTok. Dans un contexte hostile aux pratiques sexuelles entre personnes de même sexe, de plus en plus de voix s’élèvent sur les réseaux sociaux pour affirmer publiquement leur homosexualité et ou leur trans identité. Les coûts de cette visibilité sont pourtant considérables. Les réseaux sociaux sont-ils en mesure d’influencer les perceptions, les attitudes et les politiques vis-à-vis des minorités sexuelles et de genre au Cameroun?
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".