Le processus de recomposition agricole : enjeux et défis pour le développement des localités rurales fragiles.. Le cas des milieux en restructuration de la région Chaudière-Appalaches au Québec
Bibliographic record
Abstract
A l’instar des autres societes avancees, l’espace rural quebecois a subi, au cours du dernier siecle, d’importantes mutations economiques. La recomposition du secteur agricole constitue certes, l’un des changements les plus importants que le Quebec a connu depuis les 50 dernieres annees. L’objectif de cet article est d’identifier et de caracteriser les principales transformations qui ont affecte l’activite agricole dans les localites fragiles de la region Chaudiere-Appalaches. L’analyse, qui porte sur la periode 1951-1996, est effectuee sur le base de quatre indicateurs : le nombre d’agriculteurs et les emplois relies a l’agriculture, le nombre et la taille des fermes, l’ecoumene et le capital agricole. Ce processus de restructuration a favorise l’emergence d’une agriculture parallele dont les retombees sont peu significatives pour les milieux en restructuration en raison des nombreuses difficultes auxquelles ils sont confrontes. En guise de conclusion, l’auteur propose quelques pistes de reflexion afin de reorienter l’agriculture dans ce segment de l’espace rural quebecois. Elles concernent notamment le developpement de nouveaux creneaux et l’elaboration d’une politique agricole orientee vers la correction des problemes structurels de ces milieux fragiles.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".