L’éducation des fermiers, leur âge et la productivité des intrants agricoles selon la dimension des fermes laitières : le cas de la région « 04 », Québec : Farmer's education, their age and the productivity of agricultural inputs according to milk farm sizes: the case of region "04", Quebec.
Bibliographic record
Abstract
In this paper, we try to explain the effect of the farmer's education and the age factor upon the gap with respect to optimal productivity of inputs used in the agriculture of region "04", Quebec. Two models are developed. One, the "worker effect", studies the effect of education and age on labor productivity. The other, identified as the "allocative effect", attempts to describe the "worker effect" on the allocation of physical inputs. The results demonstrate that the impact of age and education on factor productivity varies with farm size. In particular, while education shows increasing returns to scale, physical inputs tend to experience constant or even decreasing returns to scale. The study concludes by advocating the development of strategies aiming at offering more educational opportunities as well as better sources of information to farmers, so that they can improve their decision-making process and the over-all productivity of physical inputs used on farms.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".