Saving the Farm: A Comparative Analysis of the Farmers’ Creditors Arrangement Act in Manitoba and Ontario
Bibliographic record
Abstract
The Great Depression and Dust Bowl of the 1930s caused great hardship for many Canadian farmers, especially in the Prairie Provinces. In response to falling prices and crop yields, as well as increasing debt levels, Parliament enacted the Farmers’ Creditors Arrangement Act (FCAA). The mandate of the bold, new statute was to keep farmers on the land by reducing and rescheduling debts to suit the productive value of the farmland and the capacity of the farmer to pay. There is little academic scholarship that examines the FCAA and how it functioned in practice. This article builds on an earlier pilot study of FCAA case files in two Manitoba counties and widens the empirical lens to consider applications from several more Manitoba counties as well as two Ontario counties. It offers the first analysis of how the FCAA operated in Ontario, employing both quantitative and qualitative data to provide a rich commentary, using examples of actual farmers. The analysis reveals that the application of the FCAA was strongly influenced by local, county-level factors. Rather surprisingly, there were few factors that can be attributed to differences between the two provinces more generally, notwithstanding the fact that there are notable variations in farming practices, operations, and conditions in Ontario, a non-prairie province, and Manitoba, a prairie province. A secondary finding is that, in general, the compromises formulated under the FCAA were highly tailored to the individual farmer’s circumstances. However, there were nevertheless pockets of case files where a fairly uniform approach was used to resolve the financial hardship of farmers who were, seemingly, all in quite similar circumstances. Accordingly, the picture that emerges is complex. FCAA practice evinces stark contrasts — generating compromises which could be either bespoke or boilerplate — and limiting the extent to which one can generalize based on the empirical data from individual counties or regions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".