Bibliographic record
Abstract
Nos villes en sont-elles a l’ere de la civilisation du loisir? Quelles sont les incidences sur les services publics de loisir? La plupart d’entre nous, constatant la hausse du nombre d’heures de travail, sont convaincus que non. Pourtant, urbanistes, economistes et sociologues reconnaissent que le loisir a envahi et remodele la ville, qu’il represente une part importante de l’economie et que les valeurs qui le caracterisent, dont la qualite de vie, impregnent meme le milieu de travail. Nous ne sommes peut-etre pas au paradis terrestre a contempler le monde en un eternel loisir, mais le loisir est une realite bien installee dont la place n’est plus a faire dans la ville contemporaine. Il semble bien que la cite du loisir soit en construction acceleree autour de nous. Mais qu’est-ce que la cite du loisir? Comment se deploie-t-elle actuellement? Comment ces changements influencent-ils l’offre en loisir? Quel est le role des professionnels en loisir a cote des urbanistes et des amenagistes, au milieu des industries du tourisme et du spectacle? Ces questions ont ete traitees dans le numero Hiver-2013 de Agora Forum qui a questionne Gerard Beaudet, professeur titulaire a l’Institut d’urbanisme de la Faculte de l’amenagement de l’Universite de Montreal, et consulte les travaux de Maria Gravari-Barbas, directrice de l’Institut de recherche et d’etudes superieures du tourisme a l’Universite de Paris1 .
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".