Guide de visionnement critique des médias, tome 1 : par la réflexion individuelle et en groupe.
Bibliographic record
Abstract
Le Guide de visionnement critique des médias d’Ina Motoi s’ouvre sur cette question : « Vivons-nous dans un monde où le temps passé devant les écrans peut facilement dépasser, pour de plus en plus d’individus, le temps écoulé sans écrans ? » (p. 1). Selon la professeure en travail social au Département de développement humain et social à l’Université du Québec en Abitibi-Témiscamingue, les individus passent de plus en plus de temps à utiliser des plateformes médiatiques. Ce phénomène tend à s’amplifier, notamment avec la crise sanitaire de 2020-2022 qui contraint une grande partie de la population à travailler, à se divertir et à communiquer avec ses proches à l’aide d’écrans. Cette surexposition médiatique ne serait pas, selon l’auteure, sans impact sur les individus.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".