“No T, No Shade, No Pink Lemonade”: An Auto-theoretical Analysis of Student Feedback to Queer Content in Dietetics Education
Bibliographic record
Abstract
LGBTQ+ inclusion in dietetics education is essential to fostering culturally safe and structurally responsive learning environments. Drawing on auto-theory informed by poststructuralism, I reflect on a piece of student feedback that described the LGBTQ+ content in my course as excessive and suggested that such excess denied discussion about other historically excluded groups. This feedback illustrates how cis-heteronormative assumptions shape expectations about what counts as appropriate, balanced, or necessary content in professional education. Using the queer expression “No T, no shade, no pink lemonade,” I explore how power, discourse, and affect emerge in moments of discomfort and curricular critique. I consider how such moments offer opportunities for reflexivity and deeper attention to the norms that shape belonging, visibility, and legitimacy in professional training. This paper contributes to ongoing efforts to embed cultural safety into dietetics curricula in ways that are reflexive, relational, and justice-oriented.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".