Cultural Diversity and Critical Dietetics: A Scholarship of Cross-Cultural Engagement
Bibliographic record
Abstract
As every human society has developed its own ways of knowing nature in order to survive, dietitians can benefit from an emerging scholarship of “cross-cultural engagement” (CCE). CCE asks dietitians to move beyond the orthodoxy of their academic training by temporarily experiencing culturally diverse knowledge systems, inhabiting different background assumptions and presuppositions of how the world works. Although this practice may seem de- stabilizing, it allows for significant outcomes not afforded by conventional dietetics scholarship. First, culturally different knowledge systems including those of Africa, Ayurveda, classical Chinese medicine and indigenous societies become more empathetically understood, minimizing the distortions created when forcing conformity with biomedical paradigms. This lessens potential for erroneous interpretations. Second, implicit background assumptions of the dietetics profession become more apparent, enabling a more critical appraisal of its underlying epistemology. Third, new forms of post-colonial intercultural inquiry can begin to develop over time as dietetics professionals develop capacities to reframe food and health issues from different cultural perspectives. CCE scholarship offers dietetics professionals a means to more fully appreciate knowledge assets that lie beyond professionally maintained parameters of truth, and a practice for challenging and moving boundaries of credibility.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.119 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.006 | 0.009 |
| 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".