LE DÉVELOPPEMENT TERRITORIAL DURABLE AUX ÉCHELLES INFRANATIONALES : LE CAS DU PROGRAMME DE FORMATION MÉDICALE À SAGUENAY (PFMS)
Bibliographic record
Abstract
L’auteur presente une analyse, realisee a partir de la perception des acteurs, de la contribution d’un projet au developpement territorial durable d’un territoire a l’echelle infranationale. Apres une breve presentation du projet, de la methodologie et de la dynamique de developpement du territoire d’accueil, il fait l’analyse de la contribution du projet a l’amelioration de l’efficacite economique, l’amelioration de l’equite sociale, le developpement des capacites de mobilisation des acteurs et de la gouvernance, le maintien de l’integrite de l’environnement et du patrimoine collectif du territoire. La capacite de repositionnement strategique du territoire, une repartition geographique equitable des biens publics qui contribuent de facon significative a la satisfaction des besoins prioritaires de la population presente et future, une mobilisation des acteurs du territoire et une capacite organisationnelle pour obtenir la collaboration de partenaires externes, le maintien et le developpement du patrimoine collectif apparaissent comme des contributions importantes a une strategie de developpement territorial durable.
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 | 0.004 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".