Bibliographic record
Abstract
Abstract Although writing about the importance of food systems for urban planners, Jerome Kaufman influenced the thinking of policy leaders and practitioners across the world including Dr. Wayne Roberts, the author of this chapter. A remarkable food policy leader in his own right, Dr. Wayne Roberts authored this chapter shortly before his passing, reflecting on Kaufman’s influence on the field and his own work. Roberts wrote that “the lack of imagination [in city government] resulting from professional over-specialization is a major barrier to more interactive conversation, learning, and partnership among city planners and Good Food advocates.” Roberts critiques the narrow ‘supply chain’ or ‘nutritionism’ approaches to understanding urban food systems. Rather, he argues that a broader view where “food’s many contributions to personal, psychological, cultural, spiritual, social, environmental and economic development of people, and the mooring of people in their time and place” ought to drive how cities view food. Roberts’ policy leadership in Toronto and Kaufman’s scholarship represent the best of what is possible in municipal policy through open-minded thinking and strategic action. This chapter, Dr. Roberts’ last piece of formal writing, leaves readers with rich ideas for developing people-centered municipal food policy. To learn more about food policy in Toronto, please contact the corresponding author.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".