Wearing Two Hats: An Interdisciplinary Approach to the Millinery Trade in Ontario, 1850-1930
Bibliographic record
Abstract
This paper combines the methodologies of historical research and material culture analysis to explore the history of the millinery trade in Ontario. The material studied is a collection of five-hundred hats, from the 1920s to 1930s, old stock from one Sarnia millinery shop. Documentary research in archives and libraries, tracing the rise and fall of the millinery trade from 1850 to 1930, provided a context for the collection. The collections research confirmed the documentary story, but also provided insights into the millinery trade not available from the written record. Resume L'auteure aborde l'histoire du commerce des chapeaux feminins en Ontario en combinant les methodologies de la recherche historique et de l'analyse de la culture materielle. Son etude porte sur une collection de cinq cents chapeaux des annees 1920 et 1930 qui formait l'inventaire d'une modiste de Sarnia. La recherche documentaire, effectuee dans des archives et bibliotheques, a permis de suivre l'essor et le declin de la chapellerie feminine de 1850 a 1930 et de situer la collection dans un contexte. La recherche fondee sur la collection a confirme le recit documentaire mais aussi eclaire des aspects de la chapellerie que les ecrits ne pouvaient reveler.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".