Pas tous dans le même bateau face à la pandémie – Lorsque la distanciation physique rend visibles les inégalités entre les étudiant.es de l’UQAC
Bibliographic record
Abstract
Afin de documenter les inégalités révélées ou exacerbées par la crise de la COVID-19, et de mesurer les impacts des mesures de distanciation sur la santé physique et mentale des étudiant.e.s, nous avons mené une étude mixte (qualitative et quantitative) afin de collecter des données auprès de 413 étudiant.e.s en provenance de 6 établissements du Réseau de l’Université du Québec (UQAC, UQAR, UQO, UQAM, UQTR et INRS). Nous publions ici les premiers résultats de cette étude, portant uniquement sur les réponses obtenues auprès de 123 répondant.e.s inscrit.e.s à temps plein ou à temps partiel dans un programme de l’UQAC, au semestre d’hiver 2020. Les résultats témoignent des inégalités vécues, de même que des défis spécifiques soulevés au moment de la collecte de données.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".