Framing Food Geographies: Framing analysis, food distancing, and the democratic imagination in rural and urban Ontario, Canada
Bibliographic record
Abstract
The current global food system is market-driven and depends on the exploitative commodification of our basic need to eat. It has been consistently condemned for its incapacity to account for justice, sustainability, welfare, and health. Developing alternative food system strategies is a necessary step towards creating a more sustainable and just reality. By conducting a comparative analysis using semi-structured interviews and virtual mapping between a rural area and an urban city in Ontario, Canada, the relationship between food geographies and the development of diagnostic (problem-oriented) and prognostic (solution oriented) framings within the corporate food regime is explored. Considering the influences of socio-geographical context (i.e. urban or rural), and the impacts of cognitive and physical food distancing adds new perspective and considerations to the existing literature. The results found that the urban participants had more robust diagnostic and prognostic framings than the rural participants. They also found that the impacts of food distancing were represented by the participants differently; The urban participants experienced more significant cognitive and physical distancing, but were mostly worried about the impacts of cognitive food distancing, whereas the rural participants were mostly focused on the impacts of physical distancing and were less affected by both types of distancing.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".