Bibliographic record
Abstract
En science sociale, l’intelligence artificielle (IA) a été étudiée à travers plusieurs champs d’étude complémentaires. D'abord, les conditions de développement de l’IA, qui met en lumière les biais sociotechniques, les formes de travail invisibilisé, les rapports de pouvoir et les enjeux éthiques liés à sa production. Ensuite, un deuxième champ s’intéresse aux usages de l’IA, en analysant la manière dont les technologies sont appropriées, détournées ou transformées par les usager·e·s. Un troisième champ explore les récits et les imaginaires associés à l’IA, entre visions utopiques et scénarios dystopiques. Enfin, un dernier champ aborde les enjeux épistémologiques que soulève l’IA, en interrogeant ses effets sur les méthodes, les paradigmes scientifiques et les conditions de production des savoirs dans les sciences sociales.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".