Life of Bryan: Working the magic of sustainable food's sweet spot, Part 2
Bibliographic record
Abstract
Bryan Gilvesy is one of Canada’s most-recognized farm innovators, as well as one of the country’s best-known leaders of the food movement. That combination is unusual in any region or country—one of the ways that Gilvesy exemplifies both the hallmarks of the food movement in Canada, as well as the unique components of agro-ecology as it emerges in a temperate-cold climate. This portrait of a food and farm leader is based on my own reporter’s notes taken over seven years of attending meetings where Gilvesy has spoken, and on files of news clippings and academic articles related to the farming methods he’s pioneered in Canada. Part 1 of this article provided an overview of Gilvesy’s background and personal evolution prior to his adoption of views and practices for which he’s presently renowned. Part 2 introduces his measures to secure a wrenching shift in food system redesign—specifically the provision to pay farmers for ecosystem services they produce on the working landscape of their farm. Part 2 will also spell out specific trends within Canada’s food movement, such as its promotion of concrete, positive and practical reform measures and its service as a Big Tent coalition of various public interest groups—trends that Gilvesy personifies. Part 3 will examine the potential of establishing a fee for environmental services through public policy rather than the marketplace.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".