Bibliographic record
Abstract
Key issues in the current agricultural debate include the future of family farms, levels of government support for farms, the roles of marketing institutions and the effect of new trade arrangements. In part, these issues have arisen because of recent price volatility, but the agricultural debate has also raised basic questions: Can farming in Canada survive, and if so, what will the new farms look like? The future of farming is approached through evidence on land values and assessments of alternative land use. The future structure of farms is approached through a review of farm size, location and product mix. Farm sizes are increasingly bi-modal, with small farms relatively insulated from farm markets, and large specialized farms dependent on the market for a narrow range of commodities. Policy changes influencing product mix or regional specialization are also reviewed. About one-half of farm output in Canada now comes from the prairie region of Canada. Open trading relationships and subsidy changes are further modifying the regional location of farming. Changes in marketing board arrangements and withering of prime farmland restrictions will lead to further shifts. The following appear to be key factors in assessing future directions for farming and farm structure: For small farms, numbers are not declining, but these operations contribute relatively little to farm output. For commercial farms, technology and scale factors are leading to larger sizes and increasing specialization. Specialization is expected to occur regionally as well as within farms, and the prairie provinces are likely to become an increasingly important part of Canadian agriculture. The sizes of commercial farms are such that few farms will be financed by single families, and the balance sheets as well as the management structures of new commercial farms will increasingly mirror those in the non-farm economy.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".