Bibliographic record
Abstract
Off‐farm labor supply in Canada is modeled using separate off‐farm labor participation and off‐farm labor supply equations, which allows variables to affect participation and labor supply differently. The data used in this study are from Statistics Canada's Agriculture‐Population Linkage Database, which links the Population Census for 1986 to a 20% sample from the Census of Agriculture. Results indicate that age, education and wages have large, significant and opposite effects on participation and supply, and that government efforts to stabilize and supplement farm incomes through rural employment programs may have less effect on labor allocation decisions than do the underlying demographic factors and regional and farm characteristics. Nous modélisons id les disponibilités d'emploi extérieur (hors‐ferme) pour les agriculteurs, utilisant des équations distinctes pour la participation aux emplois extérieurs et pour l'offre des emplois extérieurs, ce qui permet de laisser les variables influer différemment sur les deux éléments. Les donées utilisées proviennent de la base de données de Statistique Canada sur le couplage agriculture‐population, laquelle relie le recensement de la population de 1986 à un échantillon de 20 % prélevé sur le recensement de l'agriculture. Les résultats font voir que l'âge, le niveau de scolarisation et les salaires ont de grands effets, significatifs mais opposés, sur l'utilisation et sur les disponibilites d'emplois extérieurs et que les initiatives de‘État pour stabiliser et compléter le revenu agricole au moyen de programmes d'emploi rural auraient mains d'effets sur les décisions d'attribution des emplois que les facteurs sous‐jacents relevant de la démographie et des caractéristiques particulières de chaque région et exploitation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".