Arunima Datta. <i>Waiting on Empire: A History of Indian Travelling Ayahs in Britain</i>.
Bibliographic record
Abstract
As Arunima Datta relates in her compelling study, Waiting on Empire: A History of Indian Travelling Ayahs in Britain, British passengers embarking on ships to or from India in the era of the East India Company and the Raj would likely have encountered traveling ayahs on board. Well-heeled families of imperial administrators, planters, merchants, and military men often recruited these nannies from the ranks of domestic ayahs in India through the medium of brokering agencies or help-wanted ads. The hired women (and a tiny number of men) were typically aged between twenty and fifty, often married or widowed, usually childless, often Christian. They signed up for a voyage around the cape that would last seven months or more in the age of sail but just two or three weeks with the advent of steam and the opening of the Suez Canal in 1869. From then until the 1940s, the average passenger ship would have had two or three ayahs on board, usually appearing anonymously on the ship’s manifest (“Mrs. Payne’s Ayah”) or by their first name only (“Mary Ayah”). Most of the time they traveled deck class, often on rolled-out mattresses, and their duties were diverse and onerous: nannies to the children and cooks, laundresses, and maids-of-all-work to the adults. But they were better paid than their domestic counterparts, there apparently was no shortage of recruits, and some ayahs made the trip back and forth between India and Britain—and sometimes on other routes as well—more than fifty times.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".