Prejudiced Commodities: Understanding Knowledge Transfer from India to Britain through Printed and Painted Calicoes, 1720-1780
Bibliographic record
Abstract
The eighteenth-century trade in calico between Europe and India was a function of global textile manufacture, exchange, and consumption on multiple levels. This trade had several political, cultural, and economic consequences— the most important of which, I suggest, was the transfer of useful knowledge from artisanal oral textile traditions in India to the receptive, commercial, and nascent cotton printing industry in Europe. This paper explores the contribution of Indian cotton printing knowledge towards the development of Europe’s cotton industry and, consequently, its dissemination through European knowledge networks. In particular, the largely overlooked chemical knowledge pertaining to dyes and mordants responsible for the vibrant colors which gave these textiles their revered status has been analyzed in this paper. As Giorgio Riello has theorized, the trade was an apprenticeship for Europe—in design, material, technique, and taste. This apprenticeship culminated in one of the pioneering industrial sectors during the industrial revolution. What, then, was the source of the technical and material knowledge that could be codified to such an extent, and who were the hitherto hidden artisans responsible for its generation? . The sources and methodologies used in this paper reflect the multiple paradigms and contextual factors at play. For tacit and oral knowledge collected by traders and merchants in India, trade records, travel accounts, printed cottons and their tools, as well as the dyeing samples have been researched. Furthermore, to understand the development of this knowledge into codified and prescriptive systems, recipe books, craftsmen’s manuals, patents, and instructional texts have been researched. Taking into account the agency of the Indian manufacturers, this work forces us to reassess the technical and material superiority of the European cotton industry and give due credit to the complex global knowledge networks in a more decentralized manner.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".