La place de la conception des technologies éducatives dans les inégalités socionumériques d’usage
Bibliographic record
Abstract
L’étude des inégalités socionumériques depuis les années 1990 a connu des développements théoriques et empiriques soutenus qui ont contribué à consolider ses assises scientifiques et à légitimer sa pertinence sociale, notamment en éducation et en formation. En revanche, elle a peu interrogé l’amont des inégalités socionumériques d’usage de sorte que le rôle de la conception dans la fabrique des inégalités socionumériques en éducation et en formation a peu été exploré. Aussi, l’objectif de cet article théorique est de clarifier la relation entre la conception des technologies éducatives et les inégalités socionumériques d’usage en éducation et en formation afin de mieux comprendre comment la première participe des secondes. Pour ce faire, nous mobilisons la métaphore du script proposée par Madeleine Akrich (1987) et reprise sous l’angle des rapports de pouvoir par les études féministes du Social Shaping of Technology (SST).
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".