A Public Policy Advocacy Project to Promote Food Security
Bibliographic record
Abstract
To achieve food security in Canada, comprehensive approaches are required, which involve action at the public policy level. This qualitative study explored the experiences of 14 stakeholders engaging in a 9-month participatory public policy advocacy project to promote community food security in the province of Alberta through the initiation of a campaign to develop a Universal School Food Strategy. Through this exploration, four main themes were identified; a positive and open space to contribute ideas, diversity and common ground, confidence and capacity, and uncertainty. Findings from this study suggest that the participatory advocacy project provided a positive and open space for stakeholders to contribute ideas, through which the group was able to narrow its focus and establish a goal for advocacy. The project also seems to have contributed to the group's confidence and capacity to engage in advocacy by creating a space for learning and knowledge sharing, though stakeholders expressed uncertainty regarding some aspects of the project. Findings from this study support the use of participatory approaches as a strategy for facilitating engagement in public policy advocacy and provide insight into one group's advocacy experience, which may help to inform community-based researchers and advocates in the development of advocacy initiatives to promote community food security elsewhere.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".