Facilitating Think Tanks to Guide Action and Advocacy in Canadian Teachers’ Unions
Bibliographic record
Abstract
Among the inequities that have been exposed and amplified by the COVID-19 pandemic, access to affordable and healthy food is a growing crisis for many students and their families.In Canada, a survey conducted in May 2020 found that almost one in seven (14.6 percent) Canadians were living in a house hold with food insecurity, an increase from 10.5 percent in 2018, and with higher rates for house holds with children than those without. 1 This has contributed to increased attention to the impact of COVID-19 and food security on children's learning and well-being as well as renewed calls for a national school food program. 2 Canada is the only G7 country that does not have a national school food program, and about one in four children attend school hungry on any given day.Advocacy for a national school food program has persisted over several decades and united a variety of groups, including antipoverty organizations, food banks, public health and food policy experts, teachers, parents, and others.The COVID-19 pandemic has amplified this advocacy.While the federal government announced plans to work toward creating a national school food program in Canada in its 2019 budget, pro gress remains slow.3 The current landscape of Canada's school food programs can best be described as a patchwork system, varying greatly between provinces and territories and from school to school.Most school food programs are 13
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.043 | 0.018 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".