Bibliographic record
Abstract
Le budget du Quebec, pour la majorite des citoyens, reste un document un peu mysterieux?: des montants astronomiques y sont mentionnes, des evaluations tres precises d’actions non encore entreprises y apparaissent, des projections des revenus et des depenses pour les annees a venir y sont presentees. Tout cela suppose un travail considerable de la part des elus et des fonctionnaires sans que l’on sache vraiment ce qui est du ressort des uns et des autres. Qui fait quoi dans la mecanique budgetaire?? Comment les decisions sont-elles prises?? Dans son ouvrage, l’auteur decrit et explique les diverses phases de preparation et de suivi du budget du Quebec en identifiant les responsabilites des differents acteurs impliques et en dechiffrant les mecanismes de coordination qui permettent d’ajuster leurs interventions. Il cherche a faire comprendre que cet acte previsionnel qu’est le budget n’est pas le fruit aleatoire d’un exercice qui ne repond a aucune norme particuliere. Au contraire, ses assises legales sont multiples et encadrent fortement sa preparation. Le principe de realite est au cœur de cet exercice et les orientations budgetaires que le gouvernement se donne doivent tenir compte des besoins a moyen et a long terme de la population et de l’economie.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".