Bibliographic record
Abstract
This article traces the origins of the mannequin and challenges the gender assumptions it has been cloaked in. In nineteenth-century Paris, the fashion mannequin became a key technology in the construction of normative bodies, a principal “actor” in shaping current clothing cultures, and literally embodied debates over creativity and commodification. It locates the origins of the mannequin and the advent of live male fashion models in the bespoke tailoring practices of the 1820s, several decades before the female fashion model appeared on the scene. It ties the mannequin to larger shifts in the mass-production, standardization, and literal dehumanization of clothing production and consumption. As male tailors were put out of business by the proliferation of mass-produced clothing in standardized sizes, innovators like Alexis Lavigne and his daughter Alice Guerre-Lavigne made, marketed, and mass-produced feminized mannequins and taught tailoring techniques to and for a new generation of women. Starting in the 1870s and 80s, seamstresses used these new workshop tools to construct and drape innovative garments. Despite the vilification of the mannequin as a cipher for the superficiality and lack of individuality of fashionable displays in the modern urban landscape, early twentieth-century couturières like Callot Soeurs and Madeleine Vionnet ultimately used mannequins to produce genuinely creative clothing that freed the elite female body and allowed it new forms of mobility.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".