Cooking from the bottom-up: an exploration into the use of Vancouver's community kitchens as an empowerment tool
Bibliographic record
Abstract
The purpose of this thesis was to explore the effectiveness of community kitchens as an empowerment tool. This thesis observes the ways in which community kitchens empower their participants, how they are being used to foster community development, and the opportunities and constraints in using community kitchens as an empowerment tool. The research questions are addressed in a variety of ways. First, a comprehensive literature review was undertaken to define the meaning of empowerment and community development as used by this thesis. Secondly, a multiple case study approach involving participant observation, key informant interviews and a survey was conducted to examine these questions. A total of seven community kitchens were involved in the case studies. The research suggests that community kitchens do empower participants but at an individual level. Participants learn skills such as cooperation, cooking and socialisation, and are empowered through self-help and by gaining confidence and self-esteem. At a community level, efforts have been made to empower the community and contribute to community building processes but with limited output. In some community kitchens, community development initiatives (such as volunteering to cook for a larger community) are in place but community kitchens as a whole has a minimal effect in creating community. Community kitchens, however, are effective at empowering individuals which is considered the first step to community empowerment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| 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".