Planning for whom? The practice of cultural inclusion in alternative food initiatives in Metro Vancouver
Bibliographic record
Abstract
As part of a social movement to challenge and transform the dominant agrifood system, alternative food initiatives (AFIs) strive to create more socially and environmentally just food systems through policy change and programming. In a culturally plural context, processes need to be in place to ensure change efforts consider the perspectives and priorities of individuals from diverse backgrounds, including from diverse racial, cultural, and ethnic backgrounds. This thesis calls attention to the approaches and outcomes of AFIs towards cultural inclusion and racial justice through two case studies. The first is an analysis of the approaches to cultural inclusion by four food policy councils in Metro Vancouver. The second takes a closer look at one AFI, the Richmond community garden program, to better understand how garden participants navigate and benefit from the convergence of difference in public gardens. Through interviews, participant observation, and document analysis this thesis exposes the complexity of shifting towards culturally inclusive practice and provides key learnings for AFI practitioners as they strive towards more culturally inclusive outcomes in their own context.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".