Naming Nutrition Injustice: How Might Dietitians Articulate a Socially Just Dietetic Practice?
Bibliographic record
Abstract
This paper invites readers to consider how the ideals, concepts, and language of nutrition justice may be incorporated into the everyday practice of clinical dietitians whose work is often carried out within large, conservative, primary care institutions. How might clinical dietitians address the nutritional injustices that bring people to their practice, when practitioners are constrained by the limits of current diagnostic language, as well as the exigencies of their workplaces. In the first part of this paper, I draw on Cadieux and Slocum’s work on food justice to develop a conceptual framework for nutrition justice. I assert that a justice-oriented understanding of nutrition redresses inequities built in to the biomedicalization of nutrition and health, and seeks to trouble by whom and how these are defined. In the second part of this paper, I draw on the conceptual framework of nutrition justice to develop a politicized language framework that articulates nutrition problems as the outcome of nutritional injustices rather than individuals’ deficits of knowledge, willingness to change, or available resources. This language framework serves as a counterpoint to the current and widely accepted clinical language tool, the Nutrition Care Process Terminology, that exemplifies biomedicalized understandings of nutrition and health. Together, I propose that the conceptual and language frameworks I develop in this paper work together to foster what Croom and Kortegast (2018) call “critical professional praxis” within dietetics.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.088 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".