Complexity and Obsolete Data Concepts: Canadian Farm Policy, and the Changing Structure of Agriculture
Bibliographic record
Abstract
Agricultural data systems remain based upon now obsolete concepts. In particular, the "full-time, family farm" is still organizing concepts for much of the farm data system, and for agricultural policies. Yet farming has clearly bifurcated into: a relatively small number of large farms that produce the majority of the food and fiber; and a large number of small part-time farms that depend mainly on off-farm income for household well-being. Both types are family farms, but they are not the family farms of the past. It is broadly recognized that large farms pose complex challenges for data collection and policy. But small farms are also complex. While small farms may not account for much production they are important for land use issues and for maintaining political support for farm policy. As agricultural policy evolves beyond support commodities it is important to have a better understanding of the heterogeneity of agriculture. This will require more attention to how we define farming, farmers and the objectives of policy.
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 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 | 0.026 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.025 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".