‘We are all in this together’: Investigating alignments in intersectoral partnerships dedicated to K-12 food literacy education
Bibliographic record
Abstract
BACKGROUND: Activities to foster food literacy in young people are increasingly common in schools, driven both by the public health sector and by curriculum mandates from education officials in government. In Canada, both Kindergarten-Grade 12 (K-12) classroom teachers and educators from community organisations deliver food literacy education programmes in schools, often framed as partnerships working in the interests of young people. OBJECTIVE: The study examines the alignment between what both classroom teachers and community educators state are the desired outcomes for students of their food literacy education work and the topics/activities they engage in with students. DESIGN SETTING AND METHOD: We surveyed and interviewed teachers and community educators in British Columbia, Canada, and utilised participant observation and secondary data from food literacy education network activities. RESULTS: Shared food literacy education goals and topics/activities were evident in the responses of classroom teachers and community educators. Teachers framed their food literacy education programmes around the curriculum-as-plan - in this case, the provincial curriculum known as the BC Curriculum - and then enacted a lived curriculum that students experienced in the classroom. Community educators offered programmes that were initially designed to meet their organisation's focus, but which varied in terms of how much of the BC Curriculum was addressed. CONCLUSION: Our results show broad alignment between teachers and community educators in food literacy education goals and practices; however, there is potential to increase this alignment and build stronger partnerships that support teachers in enacting the BC curriculum and meeting the needs of their students.
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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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".