A Well-Dressed Woman Who Will Not Work: "Petimetras", Economics, and Eighteenth-Century Fashion Plates
Bibliographic record
Abstract
Un discurso sobre el peligro econ?mico de las mujeres que se dedican a la moda, y en particular a la compra de tejidos importados y de lujo, se desarrolla durante el siglo XVIII a trav?s de producciones de muy variada ?ndole, desde las estampas de modas hasta los tratados sobre el comercio. Im?genes verbales y visuales de petimetras sirven a autores y artistas para elaborar a la vez una cr?tica de la mujer que rechaza una dedicaci?n moderadora a la casa, la familia y la econom?a dom?stica, y una serie de proposiciones sobre la relaci?n mujer-econom?a nacional que implicar? la incursi?n en el mercado laboral de teor?as ilustradas sobre el comercio, y la apertura de mercados tales como el (important?simo durante el siglo XVI ) de los tejidos. En estas im?genes, los detalles que se refieren a los tejidos sirven a diversos autores en el desarrollo del debate sobre la relaci?n mujer-econom?a. Desde luego, las estampas de modas comunican sus propias ideas sobre fos relaciones entre la mujer y el consumo de tejidos. La imagen de la petimetra funciona dentro de textos visuales y verbales en la elaboraci?n de varias ideas tocantes al problema de la mujer que no trabaja y su falta de contribuci?n a la econom?a nacional, un sector considerable de cuya prosperidad se basaba en la producci?n y venta nacional de los tejidos de moda.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".