Problematizing Social Uses of Information and Communication Technology: A Critical French Perspective
Bibliographic record
Abstract
In France, the study of social uses of ICTs has given rise, since the 1980s, to a community of researchers referred to as the “sociology of uses” (sociologie des usages). Under this common label, many sociological works have been developed, primarily focused on the concept of use. In this article, we would like to briefly recall the main theories which have been the basis of research concerned with the social materiality. We would then like to recall some of the scientific requirements for the conduct of research that aim to describe and explain the social uses of ICT in an empirical and theoretical dialectic. Finally, we suggest a few ways to support a critical sociology of uses that could assess the social facts it examines.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.017 | 0.063 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".