Why I fell in love with Critical Dietetics; A personal/political narrative
Bibliographic record
Abstract
Critical Dietetics found me at a conference in Japan of the International Conference of Dietetics where I presented a poster on my dissertation work centering around the lack of dietitians of color in the U.S. Lucy Aphramor took my card, and when the conference was to take place in Canada, she contacted me. After I read the founding documents, and articles in the first Journal, I knew I had found home. A child of the 1960's, I came to Dietetics after thirty years of social activism fighting war, racism, and sexual oppression. The perspective of my work has been centered in Marxism and Critical Race Theory. My mission as an educator is to help my students see themselves as advocates for equal access to good nutrition, health care and education for all. Critical Dietetics has allowed me a voice, in a space that loves women, food, the human body and still believes in the future of humanity.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.060 | 0.120 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.013 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".