Préparer un dossier pour les décideurs à l’aide de données sur la Toile
Bibliographic record
Abstract
PREPARER UN DOSSIER A L’AIDE DE MATERIEL DE QUALITE Les professionnels en loisir doivent regulierement preparer des dossiers a l’appui d’un projet, d’un budget ou d’un avis, soit en vue d’une reunion de conseil municipal ou de conseil d’administration, soit dans le cadre d’une demande de subvention. On exige qu’ils soient « professionnels », c’est-a-dire objectifs, et que leur argumentaire soit fonde sur des faits et sache presenter les risques comme les avantages. Ces dossiers s’inscrivent dans un processus de decision. Ils doivent, des lors, suggerer diverses solutions en faisant ressortir les avantages et les inconvenients de chacune et en donnant toutes les informations necessaires a une prise de decisions. En amont des projets, on demande aux professionnels de signaler les besoins futurs de la municipalite ou de l’organisation et de faire les recommandations qu’ils jugent appropriees, notamment quant a l’adoption de politiques visant a assurer la sante, la securite ou le bien-etre de la collectivite, ou encore a ameliorer les services. S’appuyant sur son experience et les outils qu’il a developpes, l’Observatoire quebecois du loisir presente ce bulletin hors serie, sorte de bottin des lieux et des sites sources d’informations de qualite pour soutenir les dossiers a preparer.
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 | 0.040 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.107 | 0.044 |
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".