Usages d'lnternet selon le genre et I‘âge: une double différenciation*
Bibliographic record
Abstract
At the heart of current debates on the impact of Information and Communication Technology (ICT) is the question of the importance of variations in the use of the Internet. In order to contribute to this discussion we have conducted a survey. The research instrument dealt with a week's computer usage with more than 40 possibilities of Internet use. The results show that age, more often than gender, is a discriminating variable in terms of Internet use, even if we can argue that, between the sexes, differences associated with gender stereotypes reappear. On the other hand, we argue that there is a homogenization of practices for other functions (chat, forums, etc.) and, as a whole, an attenuation of socio-economic variables. Au œeur des débats actuels sur l'impact des technologies d'information et de communication (TIC) se pose la question de l'importance et de la variété des usages d'Internet. Dans le but de contribuer à cette discussion, nous avons mené une enquête. L'instrument de collecte portait sur le temps passé, par semaine, devant un ordinateur et sur plus de 40 possibilités d'usages d'Internet. Les résultats démontrent que l'âge, plus souvent que le genre, est une variable discriminante de la consommation d'Internet, même si nous pouvons constater que, entre les sexes, des differénces associées aux stéréotypes de genre réapparaissent. Par contre, nous constatons aussi une homogénéisation des pratiques pour d'autres fonctions (clavardage, forums…) et, dans l'ensemble, une atténuation de l'influence des variables sociodémographiques habituelles.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".