The Secondhand Clothing Trade in Europe and Beyond: Stages of Development and Enterprise in a Changing Material World, <i>c</i> . 1600–1850
Bibliographic record
Abstract
Between 1600 and 1850 Europe was reshaped economically, culturally, and materially—the secondhand clothing trade was a vital factor in these events. Cloth and clothing represented among the most important and costly purchases for generations, lying at the heart of household budgeting, intersecting routinely with markets. The secondhand trade was a unique micro-enterprise vehicle, as well as a growing commerce for entrepreneurs. It stimulated retailing and featured in international trade. Throughout these centuries, the scale of secondhand commerce, circulating in and beyond Europe, expanded enormously as economies were reshaped by industrial expansion and extra-European trade. During the past decades scholars have lifted the study of the secondhand trade from its previous obscurity, recognizing its organic connection to cultures and commerce. I now propose three stages in the evolution of the secondhand clothing trade, shaped by the underlying transformations of northwest Europe, suggesting the ways in which the patterns of economic and social change affected this sector over time. The three stages are: (1) transition from scarcity, (2) growing abundance, and (3) industrial plenty. By defining these tripartite divisions we can better understand the evolution of the secondhand clothing trade, its features, and relative significance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".