Filling our plate: A spotlight on feminist food studies
Bibliographic record
Abstract
The idea for this special issue emerged from the enthusiastic response to a day-long series of sessions on feminist food studies that were held during the joint conference of the Canadian Association of Food Studies, the Association for the Study of Food and Society, and the Agriculture, Food, and Human Values Society, in 2016, in Scarborough, Ontario. The sessions brought together feminist food scholars from across Canada and the U.S. to share their work and to collectively claim space within the conference program to address feminist perspectives in food studies. For us, and the many presenters and attendees at the sessions, the opportunity to gather together and savour more than the usual one or two conference sessions devoted to feminist perspectives was a long-awaited pleasure that did not disappoint. The presenters and audience members illuminated many of the issues, complexities, and perspectives that an explicitly feminist lens brings to food studies. The energy and excitement that infused the room as each presenter shared their work filled our plates that day.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.025 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".