Contours and canyons of health: Learning health equity through body mapping
Bibliographic record
Abstract
Critical health communication (CHC) explores how meanings and enactments of health are linked to power dynamics and systemic inequalities by centering the body in research and practice. When teaching CHC in a postsecondary classroom, it is imperative to provide students with an opportunity to engage in embodied learning to think critically about the taken-for-granted assumptions about health, illness, and disabilities. This semester-long assignment employs body mapping, an arts-informed method of embodied storytelling to help students engage in intimate and affective learning of a health condition of their choice. Students conduct in-depth research about the condition, create a fictional character who lives with the condition, and produce life-size maps of the character using their own body contours.Courses This assignment is suitable for upper undergraduate or master’s level communication studies courses featuring CHC, health humanities, social determinants of health, and social justice.Objectives Upon successful completion of this semester-long project, students will be able to: demonstrate a clear understanding of how a health condition of their interest is represented, marketed, and promoted through the media; describe sociocultural, ideological, and structural forces that shape media representations of the health condition; ethically and compassionately represent the health condition as a holistic experience through a fictional character they develop; create a life-size body map of the fictional character that embodies the health condition; and critically reflect on their semester-long learning and articulate strategies for improvement.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".