Using Participatory Action Research to Support Civil Society Action for a Sustainable Food System in Yellowknife
Bibliographic record
Abstract
Grounded in the Participatory Action Research (PAR) methodologies, this thesis examines the opportunities and challenges associated with encouraging multi-stakeholder collaboration around the vision of a more just and sustainable food system for Yellowknife, Northwest Territories (NWT).In partnership, the researcher and community research partners identified the following research question: How can community members and organizations, local businesses, and decision-makers from the City of Yellowknife collaboratively engage around the vision and principles of the Yellowknife Food Charter to improve the policy arena for a just and sustainable food system for Yellowknife?Grounded in political economy theories of sustainable food systems and governance, this thesis addresses this question through praxis -the cycle of reflection and action.To reflect, this thesis examines the role of PAR in realizing actions for the food charter, focusing on the importance of decolonizing research through trust and partnership, and on community ownership of research questions and process.It also makes recommendations for building trust in research through explicitly discussing the expectations of a PAR process with local research partners prior to and throughout the research.As action, the Coalition collaboratively identified the need for a municipal food strategy to improve the local policy arena and this thesis includes a set of policy briefs to be used by the Coalition to present to the local municipality.These briefs are contextualized by a historicized account of the Yellowknife food system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.016 |
| 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".