From Dismal Swamp to Smiling Farms : Food, Agriculture, and Change in the Holland Marsh
Bibliographic record
Abstract
Driving through the Holland Marsh one is struck immediately by the black richness of its soil. This is some of the most profitable farmland in Canada. But the small agricultural preserve just north of Toronto is a canary in a coal mine. From Dismal Swamp to Smiling Farms recounts the transformation, use, and protection of the Holland Marsh, exploring how human ideas about nature shape agriculture, while agriculture in turn shapes ideas about nature. Drawing on interviews, media accounts, and archival data, Michael Classens concludes that celebrations of the Marsh as the quintessential example of peri-urban food sustainability and farmland protection have been too hasty. Instead, he demonstrates how capitalism and liberalism have fashioned, and ultimately imperilled, agriculture in the area. The social and ecological crises of our industrialized food system are becoming more acute, and questions about where our food comes from and under what conditions have never been more important. At the centre of these questions – and of any efforts to re-localize food systems – is the land. This fascinating case study reveals the contradictions and deficiencies of contemporary farmland preservation paradigms, highlighting the challenges of forging a more socially just and ecologically rational food system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".