Weight Stigma and University Media
Bibliographic record
Abstract
Non-inclusive media images of and text pertaining to people with higher body weights can perpetuate weight stigma. This qualitative study explored the college student’s perspective of body messaging at a public university. Body messaging included images of people and text found within university print materials and social media platforms. We engaged a sample of seven participants, all of whom self- identified as a person of larger body size. The photovoice method was used for data collection where participants collected photographs of body messages the university included on print materials and social media platforms. Participants then completed an online questionnaire composed of open-ended questions about each photo. By using photovoice, we discovered the body messages participants found worthy of capturing. From the 27 photovoice body messages, over 40% did not have size diversity or if they did, they were placed in non-prominent locations on campus. Participants reported feeling excluded and hidden by the non-weight inclusive messages. Results from this study suggest that university body messaging may be part of the experience of weight stigma. Recommendations are provided for universities on how to promote weight inclusive environments through body messaging found in policy, programs and practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".