Student Engagement with Community-Based Participatory Food Security Research: Exploring Reflections through Photovoice
Bibliographic record
Abstract
FoodARC is a research hub for community-based participatory research (CBPR) contributing to healthy, just, and sustainable food systems for all. University students, largely from dietetics programs, are engaged as co-learners and research partners. This study explores the contribution of CBPR to student learning on household food insecurity (HFI) and community food security (CFS) and ways to address these issues through practice. Photovoice, an arts-informed 3-phase participatory research process, was used to take pictures that reflected student experiences and insights regarding CBPR. Through a half-day guided discussion, 5 participants shared and discussed their photos and the meanings behind them with other participants and then collectively analyzed and interpreted common themes. Three overarching themes reflecting student learning and development associated with CBPR experiences were identified: students' expanded understandings of HFI and CFS as well as potential solutions to address these issues, their modeling of participatory ways of working, and applications to future professional practices. Student understandings about HFI and CFS through the integration of a community-engaged learning environment like CBPR results in important learning and personal and professional development. Learning is enriched and students are able to imagine their roles in addressing these issues through practice.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".