Confronting Anti-Black, Anti-Indigenous, and Anti-Asian Racisms in Food Systems in Canada
Bibliographic record
Abstract
The impetus for this themed section came out of the broader reckoning that touched off in the summer of 2020 in the wake of the murder of George Floyd. The Canadian Association for Food Studies board, like so many organizations struggling to respond to such brazen violence, released a statement on racialized police violence and systemic racism. In the statement the CAFS board commits to more deliberately centering the work of anti-racism in our association̶ —and this included two shorter-term projects. Curating and publishing an open access resource list on food and racism in so-called Canada, and publishing a themed section on racism in the food system. The CFP for the special issue was released the following May, 2021, and read in part “As we reckon with the ways white supremacy, capitalism, patriarchy and colonization has shaped food systems, we must also reflect on and redress dominant modes of thought and approaches that reproduce inequity within the academy (e.g., research and teaching) and society at large. As such, we welcome submissions that centre diverse ways of knowing and methods of knowledge production.” Over the past nearly two years, we (the special issue guest editor, Ama, and collaboration assistant, Michael) have met virtually many times to discuss the CFP, the process, the articles, and the broader backdrop of white supremacy, colonialism, and capitalism. And as we reflected on how we wanted to write this editorial, it occurred to us that our own approach to collaboration on this project has been relational, conversational. So, rather than writing a conventional editorial, we once again met virtually to reflect on some key themes that (re)emerged over the past couple of years. What follows is part of that conversation, edited for clarity and brevity. We hope this special issue contributes to keeping the conversations (and action) focused on structural change going.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.016 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".