Kitchen Table Pedagogy: A Three-way Conversation on Animating Knowing and Becoming for Health Justice
Bibliographic record
Abstract
The three of us (be)come together, yes on Zoom calls and in Google docs, and in a way that re-imagines the sitting together at a well-worn kitchen table to animate our current shared preoccupations. The table has seen many conversations before us, so it’s well-worn by feminist scholars who were also troubled, yearning, and adamant about leaving their marks. We have come to, become at, this type of table for centuries, sitting together preparing food, folding laundry, and washing up, all while also reckoning that our stories matter and can make a difference. In a context that is both practical and intellectual – centring fat liberation in public health nutrition – how do we remain good scholarly support to one another in and through challenging times? Times of dissent. Times of tedium. This chapter reveals the stories we need most now as we grapple with our places in the neoliberal university and public health pedagogy, specifically as a critique of policies that we read as non-liberatory. We grapple with how our complicity within these spaces might be considered from a place of mutual support and a shared commitment to unlearning and unsettling colonial ways of knowing, especially as we contemplate the problematics of public health imperatives regarding “intuitive eating” and health behaviour change. And we ask what this means for scholarship that is accountable to liberation, that is, personal survival and collective healing and learning.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.070 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 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".