Declining Education Levels in Young Male Farmers in Southwestern Ontario
Bibliographic record
Abstract
Core Ideas Education level is declining in young male farmers but not female farmers. Interactions between variables can be key for understanding environmental behavior. This education trends needs to factor into the design of agri‐environmental programs. Environmental decisions taken by farmers often depend on their age, gender, and formal education. Changes in these demographic variables are therefore important for designing long‐term environmental policies. However, studies on the effect of demographic variables on environmental behavior often show conflicting results. Here, we used mail survey data (n = 3069) to determine whether education levels of landowners in rural southwestern Ontario, Canada, varied with age, gender, and occupation (“farmer” or “non‐farmer”). Education level increased with decreasing age in all landowners with the exception of male farmers, where the opposite trend was observed. This striking result highlights the importance of taking into account interactions among demographic variables. The unexpected decrease in education level in young male farmers is cause for concern and may need to be taken into consideration by policymakers in the design and implementation of agri‐environmental programming.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".