Clothing Fit Issues for Trans People
Bibliographic record
Abstract
As many as nine million people identify as a transperson in the United States, yet mass clothing designing and manufacturing do not meet the needs of this consumer group. This research examines the role of fit in ready-to-wear (RTW) clothing using qualitative research methods. 90 transpeople from the United States, Canada, and Ireland participated in interviews and data from interviews were analyzed using line-by-line analysis, resulting in three themes. Theme 1 explored current fit problems with RTW clothing, Theme 2 explored the desire to use clothing to hide parts of the body that did not align with one’s gender identity, and Theme 3 explored the desire to use clothing to highlight parts of the body that did align with one’s gender identity. Findings from this research confirm the assumption that current RTW clothing does not meet the needs of the transperson population and offers areas where designers and manufactures can reassess their methods relative to this consumer group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".