Scholar-activist perspectives on radical food geography: collaborating through food justice and food sovereignty praxis
Bibliographic record
Abstract
Radical geography research, teaching, and action have increasingly focused on food systems, examining the scalar, sociopolitical, and ecological dynamics of food production and harvesting, processing, distribution, consumption, and waste. While academics have contributed significantly to these debates, the success and progress of this scholarship cannot be separated from the work of practitioners and activists involved in food justice and food sovereignty movements. This paper draws together the voices of scholars and activists to explore how collaborations can productively build the evolving field of radical food geography and contribute to more equitable and sustainable food systems for all. These perspectives provide important insight but also push the boundaries of what is typically considered scholarship and the potential for impacts at the levels of theory and practice. Reflecting on the intersecting fields of radical geography and food studies scholarship and the contributions from the scholar-activists, the authors share a collective analysis through a discussion of the following three emerging themes of radical food geography: (1) a focus on historical and structural forces along with flows of power; (2) the importance of space and place in work on food justice and food sovereignty; and (3) a call to action for scholars to engage more deeply with radical food systems change within their research and teaching process but also in response to it.
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.106 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.009 |
| 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".