Bibliographic record
Abstract
La mission de l’Observatoire quebecois du loisir (OQL) a toujours ete de fournir aux decideurs et aux acteurs du loisir public et de la societe civile des informations susceptibles d’eclairer leurs decisions qu’elles soient strategiques, techniques, financieres ou politiques. Le present bulletin s’attarde a examiner les defis et les tendances qui, au Quebec, vont influencer les orientations et les facons de faire au cours des prochaines annees. Il soumet a la discussion les 12 travaux du monde du loisir qui devraient contribuer a lui assurer une place sur le forum public du Quebec et devant Cesar ou les gouvernements. L’OQL a effectue une lecture des exercices similaires aux Etats-Unis1 et au Canada2, un examen systematique des debats actuels de societe1, une lecture des donnees et des publications sur la population quebecoise4, un bilan de la contribution gouvernementale en loisir5 et une analyse des enjeux qui interpellent la planete dans un univers de mondialisation6. Ces exercices ont permis de mettre en lumiere des defis politiques, sociaux, administratifs et environnementaux qui demandent une attention urgente. Il faut choisir : prendre en compte aujourd’hui ces defis comme specialistes du loisir ou adopter, demain, une attitude de « victimes » et laisser les autres decider de l’avenir de nos organisations. Dans ce premier bulletin, sont exposes les enjeux sociaux qui nous interpellent. Le prochain traitera des enjeux politiques, administratifs et environnementaux.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 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".