Community-Based Action Research in Vancouver Public Schools: Improving the Quality of Children’s Lives through Secure and Sustainable School Food Systems and Experiential Learning
Bibliographic record
Abstract
The “key players in a community-based action research project in Vancouver Public Schools study” is a part of the Think&EatGreen@School (TEGS) project that aims to document the experiences of important actors within the movement towards healthy and sustainable Vancouver public school food systems and related learning opportunities. By interviewing key players in the Vancouver school food movement, we found that meaningful collaboration is a critical component in creating rich learning experiences that result in a more holistic and integrated perspective on food systems and improved quality of life. The TEGS Project is guided by principles of community-based action research (CBAR), an iterative process using community-university collaboration to identify opportunities, generate knowledge, and devise and implement locally-appropriate action to create desired change. Capturing the stories and experiences of key players represents an important step in articulating the learning emerging from this collaboration. Key Players commented on important networks, challenges, “success stories,” and styles of leadership that facilitate successes. The Key Players study has assisted the Think&EatGreen@School community of learners to better understand practices that constitute ‘ seeds of change,’ to use these seeds to replicate positive actions, and to refine and strengthen the direction of the project.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".