Bibliographic record
Abstract
La population canadienne est vieillissante. Malgré cela, l’âgisme, cette forme de discrimination sur la base de l’âge se manifeste de manière explicite ou insidieuse et est encore trop peu dénoncée. Faut-il chercher les raisons de ce silence dans l’absence d’informations ou plutôt l’insuffisance de connaissances sur le vieillissement et les aînés ? Ce livre approfondit la réflexion sur l’âgisme et ouvre des pistes de solution. On y présente les manifestations et les conséquences de l’âgisme, depuis la représentation des aînés dans les médias jusqu’aux pratiques âgistes dans les soins de santé, en passant par la discrimination des travailleurs vieillissants. Sous la direction de Martine Lagacé avec la collaboration de Jérôme Pellissier, Denis Bachand, Florian Grandena, Luc Dupont, Jean-Pierre Thouez, Yves Joannette, Jean Vézina, Anne Bourbonnais, Bernadette Dallaire, Hubert Doucet, Anne-Marie Guillemard, Francine Tougas et Marcel Mérette.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".