Diagrammatic Manifestos: A Method for Studying the Fluidity of Gender in the Production of Fashion Photography
Bibliographic record
Abstract
This sociological research studies how fashion editors, art directors, and photographers make the fluidity of gender more visible within an industry established on the binary womenswear/menswear. It addresses gender fluid practices as a questioning of the conditions in which relations between body and dress are made systematic. The research has identified some of the restrictions faced when producing gender fluid fashion imagery, and highlighted the alternative solutions that originate from these limitations. This paper proposes to apply live and inventive methodological approaches to fashion studies. The design of my methodology was concerned with its capacity to study a subject still mostly understood through a binary ontology. Consequently, the “Diagrammatic Manifestos” is a research method attentive to the conditions in which relations can be made different, rather than identical, to dominant gender ideals. Throughout the series of interviews, diagrams were operated as analytical devices to graphically reorganize transcripts into manifestos. The diagrams’ forms were made responsive to the differences in each participants’ narrative and reveal how their individual experiences of gender affect the images they produce.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".