Creating learning alliances for flourishing food environmental futures
Bibliographic record
Abstract
This article emerged from a community-based symposium held in a public library, aimed at synthesizing reflections on the connections between climate actions, food security, and (im)migration. The authors, representing diverse positionalities and professional backgrounds explore the generative entanglements offered through food justice discourses and land-based pedagogies. Through channelling personal and professional experiences and disciplinary expertise, we sought to open up intersectional imaginaries of food and environmental justice, while actively seeking spaces for learning alliances. Emergent themes include challenging the settled imagination of integration in a community and on the land, finding ways of healing and placemaking through attending to the soil, plants, and other more-than-human beings that support collective well-being, and affirming the emancipatory potential of art-based learning entangled with land-based pedagogies. In foregrounding these voices, the article contributes to the ongoing efforts to support pluralistic forms of knowing and being, through exploring trajectories of transformative educational experiences centering food/environmental justice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".