Public Policy in the 21st Century: Is Prairie Agriculture Becoming Like Any Other Industry? Does it Matter?
Bibliographic record
Abstract
The question addressed in this paper is whether Prairie agriculture has become “like any other industry”. The implication is that a positive response would lead to the conclusion that it should therefore be treated “like any other industry” rather than being accorded the special status that it has enjoyed for more than a century We show that, as net farm incomes have declined over the past 35 years, Prairie farmers have responded by seeking other (non‐farm) sources of income. At the time of the 1996 census, net farm income accounted for only 31 percent of farm family income (down from 75 percent in 1967). By 1999, the contribution of net farm income was even lower than in 1996 In spite of the dwindling contribution of net farm income to farm family income, average farm family income in Saskatchewan has exceeded average provincial household income for all but two years between 1971 and 1998, Under these circumstances, it is necessary to ask why it is in the public interest to subsidize an activity which, in recent good times, produced 31 percent of the income of a subset of the population whose household incomes were 10–15 percent above the provincial average Based on income levels, it is probably no longer necessary to accord farm families special status in the public policy arena. However, other policy considerations (immobility of resources, the role of agriculture in the rural economy, environmental considerations, food safety and security, for example) remain, differentiating agriculture from other industries
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".