Bibliographic record
Abstract
Abstract To delay paying wages to seamen, the late Stuart Navy issued them instead with “tickets” to be redeemed for cash after months or years of delay. Seamen often sold the tickets at deep discounts to ticket buyers, who became government creditors for unpaid wages, one of the largest items in the national debt. Ticket buyers were savagely attacked in pamphlets. This article is a preliminary exploration of ticket buying, focusing on the large minority of buyers who were women. It shows that many of them were in fact the wives and widows of the seamen, working in the crowded streets around the Navy Office and in the cottages of the maritime communities nearby. Navy pay books are introduced as a key source; the business of one trader is evaluated using her financial papers, and the work of others assessed from probate records. Ticket buying opened up related opportunities for women as brokers of deals and as professional receivers of wages. But while pawning could be used as protection against the growing hazard of unpaid tickets, even with deep discounts it was difficult to make even a moderate return in the trade. Ticket buying was not a route to fortunes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".