Towards Just Food Futures:
Bibliographic record
Abstract
The call for Just Food Futures reflects a desire to address social inequities, health disparities, and environmental disasters created by overlapping systems of oppression including capitalism, white supremacy, and heteropatriarchy. While many food movement actors share a desire to meaningfully tackle these issues, the richness and broadness of the food movement does not come without problems. The challenge of engaging with the intersectional nature of food-based inequities is apparent in the tensions between distinctive food organizations and movements and their sometimes conflicting goals, approaches, tactics, and strategies. This Themed Section brings together some of the contributions to and reflections from a virtual three-day workshop held in May 2021 in which we aimed at better understanding the differing approaches, the spaces in which they work, and where we explored collaborative possibilities within, between, and beyond food movements. In this Introduction we share reflections from the guest editors. To explore how food movements can collaborate in solidarity while not negating differences, we first identify key frictions within and between food-related movements and why they persist. Second, we suggest three strategic orientations that may help to explore collaborative possibilities within, between, and beyond food movements: Learning from other movements, fostering political literacy, and engaging with tensions productively. Finally, we consider the role and responsibility of academics within these conversations. We close with a call for (re)politization across difference and relate this back to strategies for broader social transformations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.035 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".