Alignment of ethical guidelines for research involving transgender people and communities with critical dietetics: A declaration
Bibliographic record
Abstract
Articulating the projectMy (CM) program of research is aimed at raising awareness of the food, nutrition, and eating challenges of T+GD people and to develop relevant nutrition care guidelines (NCGs).The intent is that dietitians, administrators, and policy advocates will use the NCGs in advocating for/offering services that optimally support the T+GD community.Colleagues have expressed interest in evidence-based guidance in providing nutritional care via the Critical Dietetics, and Dietitians' Support Facebook groups, and conversations following presentations at Dietitians of Canada (DC), Critical Dietetics (CD), and the CPATH conferences.In Braindate meetings 1 with students/recent graduates as part of DC conferences, I heard reports from some practicum students of some practicum preceptors insisting on using a person's dead name and incorrect pronoun: the students felt extremely uncomfortable and powerless to speak out.These experiences, combined
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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 | 0.190 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.027 | 0.053 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".