Synthèse de la Session Semi-plénière « Sociologie dans, depuis et sur les Outre-mers », du 6 juillet 2023, Congrès de l’Association française de sociologie, Lyon
Bibliographic record
Abstract
Lors de la semi-plénière du groupe SocioOM à l’AFS 2023, quatre chercheur·ses – Audrey Célestine, Valélia Muni-Toké, Live Yu-Sion et Marine Haddad – ont partagé leurs expériences de la sociologie dans, depuis et sur les territoires dits d’Outre-mer. Ces échanges ont mis en lumière les difficultés institutionnelles rencontrées dans des contextes où les structures académiques locales en sociologie sont souvent faibles ou absentes. Les intervenant·es ont évoqué les enjeux de la production de savoirs dans ces territoires, les tensions autour de la visibilité scientifique, ainsi que les défis méthodologiques et éthiques spécifiques à ces terrains. Trois temps ont structuré la session : expériences de terrain, enjeux de visibilité, et méthodologies, avant un échange avec le public.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".