Prix de vente des fermes au Québec. Divergence de vue entre les besoins des cédants et des repreneurs
Bibliographic record
Abstract
L’écart croissant entre les valeurs marchande et économique des exploitations agricoles québécoises complexifie le processus de transfert des fermes à la prochaine génération. Cette étude applique une approche d’évaluation d’entreprises agricoles propre au contexte de leur transmission en vue d’analyser la détermination du prix de vente au transfert. Les résultats montrent que le prix de vente est significativement corrélé aux besoins de retraite nets des cédants et, de façon moins significative, à la valeur économique des capitaux propres des entreprises de l’échantillon. D’importants compromis sont réalisés en vue d’assurer le transfert et, en dépit de leur plus forte capitalisation, les fermes laitières semblent confrontées aux mêmes défis que les autres types de production.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".