Trans* inclusivity in fashion retail: Disrupting the gender binary with queer perspectives
Bibliographic record
Abstract
This study is about gender-inclusive fashion retail, with a focus on trans* inclusivity. It is based on primary and secondary research of trans* issues in fashion. This research resulted in an inclusive pop-up shop that eliminated the reinforcement of the gender binary present in conventional fashion retail. Primary research consisted of semi-structured shop-and-talk interviews with end users and industry experts. All end-user interviews were conducted in Toronto in a minimum of two different fashion retail stores, such as one department store and one gendered store. The expert interviews were conducted in a context that matched the individual, such a designer’s home studio. Secondary research used a blended framework of queer, intersectional and post-capitalist theories to analyse trans* discrimination, unisex fashion and transness in popular culture. Key themes derived from these areas were cultural variance of gender expression, lack of accurate trans* representation and superficial queer initiatives. Fashion is based on the socially constructed gender binary, which excludes trans* people and cisgender (cis) people who are gender non-conforming in dress. The heteronormative and cis-normative beauty standards of fashion shame those who do not follow them. The current trans* representation in fashion is minimal and problematic. Real trans* people and narratives are not broadcasted by mainstream media; however, tokenized trans* celebrities and cis people acting as trans* mouthpieces are. This research questions how services and environments of fashion retail can be redesigned to be gender inclusive, by normalizing disruptive gender expression and increasing trans* visibility. This research is important because of the empowerment, validation and safety that queer and trans* people deserve when in public spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 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".