Bibliographic record
Abstract
In this research I interrogate how metrics of food insecurity rely on indexes of deprivation, of which Indigeneity is deemed an indicator of social deprivation. I engage the fields of critical Indigenous studies, critical whiteness studies, and Indigenous science, technology, and society. I argue that social deprivation indexes produce and surveil “deprived” geographic food zones according to metrics of whiteness. I make three central arguments through the empirical context of food insecurity interventions for Indigenous people in Winnipeg, Manitoba, Canada. First, accounting for food insecurity through social deprivation indexes produces food insecurity because it does not accurately depict sources of food outside of what has been deemed appropriate (see: “healthy”) through logics of whiteness. Second, solely imagining food insecurity through logics of social deprivation results in interventions of whiteness, which overdetermines how inner-city urban space is designed, surveilled, and made carceral. Third, if food studies does not interrogate and make serious efforts to undo its own whiteness, it will continue to be deficient in its renderings and understanding of food geographies beyond whiteness.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".