Sovereign at heart: photovoice, food mapping and giving back in Alberni-Clayoquot
Bibliographic record
Abstract
This article reflects on a year-long project that used both photovoice and food asset mapping methods in the Alberni-Clayoquot Region of British Columbia, Canada. Following others who emphasize reciprocation in research and the application of a heart-centered approach, this work had a two-fold purpose: 1) to give back to the community in which the researcher lived and worked, and 2) to create visual material and food policy guidance for the community. The photovoice project involved participants who were growers, processors, harvesters and/or foragers of food and resulted in multiple exhibitions of their work as well as a photobook distributed within the community. Alongside these, two separate food mapping sessions were completed with 42 participants which demonstrated that many residents associated food with human health, regional ecology, localization, production methods, social relations, economics, and spirit (metaphysics). We found these associations with food appear to indicate that many envision increasing regional food production by means of increasing food sovereignty. Our results (1) confirm that the research methods of photovoice and food asset mapping complement each other, (2), demonstrate the worth of giving back and the importance of a heart-centered approach to food systems’ research and change, (3) substantiate claims that the “Seven Pillars of Food Sovereignty” transcend borders and apply within Canada.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".