Bibliographic record
Abstract
How and when can pizza be a protest? The potentials of food-in-action for cultural resurgence and community building amongst criminalized peoples are significant. That being said, attention to the ways carceral logics divide and isolate us is needed to avoid romanticizing food-based research and programming and perpetuating harmful power structures within and beyond prison walls. In a nutshell, activist research in and against carceral contexts is complicated, and adding food can make it even messier. Thankfully, getting our hands dirty and later cleaning up together after are important processes across food justice contexts. Based around a recent pizza party held as part of my ongoing doctoral Participatory Action Research, these notes from the field (or, in this case, the community kitchen) will trace the complexities of community building through cooking circles. I will share possibilities of sharing food as a radical act and the sticky parts of anti-carceral research and community organizing. Using a day spent with my co-researchers - women on parole - rolling out dough, building our pizzas, and dreaming the next phases of this project, I will share reflections on how the making and sharing of food is an apt site for disruption and resistance, the importance of centering the wisdom of people with lived and living experience and expertise of incarceration (while doing the ongoing work to confront power hierarchies and mitigate the potentials for harm), and how food justice can help harness the privilege of academic research to support resistance against the carceral state.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".