Normes de genre et subversions : le rôle des influenceuses dans le body positivisme et le féminisme numérique
Bibliographic record
Abstract
Le présent article explore le rôle des influenceuses sur les réseaux socionumériques dans la reproduction et la subversion des normes et des stéréotypes de genre. En s’appuyant sur une méthodologie qualitative comprenant des entretiens semi-directifs et une analyse de contenu, nous avons examiné un échantillon diversifié d’influenceuses actives sur Instagram, YouTube et TikTok. Les résultats révèlent une dynamique duale : d’une part, les influenceuses participent souvent à la standardisation des normes de genre dans leur quête de visibilité et de reconnaissance, d’autre part, certaines utilisent leur influence pour contester et déconstruire ces mêmes normes, notamment à travers des mouvements comme le body positivisme. Ces pratiques sont influencées par des contraintes sociotechniques, économiques et culturelles et montrent que les réseaux socionumériques permettent à la fois la reproduction et la contestation des normes hégémoniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".