“Dismantling the structures and sites that create unequal access to food:”
Bibliographic record
Abstract
In the summer of 2019, Elaine Power, Professor in the School of Kinesiology & Health Studies at Queen’s University, interviewed Paul Taylor for a research project on community food programs. Paul, a Black man, is the Executive Director of FoodShare Toronto and an anti-poverty activist. In 2020, Paul was named one of Toronto Life’s 50 Most Influential Torontonians, was awarded the Top 40 under 40 in Canada, and voted Best Activist by the readers of Now Magazine. In this interview, Paul explains his philosophy of leadership, his understanding of food justice, and the ways that non-profit organizations can contribute more meaningfully to food justice. Paul understands food insecurity as a lack of income, an issue disproportionately affecting Black, Indigenous and people of colour. Therefore, the best solution to food insecurity is a decent-paying job. Non-profits concerned about food justice must pay living wages, and close the gap between the highest and lowest paid employees. They must also listen to their clients and take their advice. Paul also explains how his background growing up poor and hungry in Toronto was his best education for his current position at FoodShare Toronto.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.063 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".