Bibliographic record
Abstract
Work as a servant, Britain I was the only servant. I had to be up at six in the morning, and there were so many jobs lined up for me that I worked until eleven o’clock at night. The mistress explained that she was very particular; the house had to be spotless always. After all, they were professional people and used to very high standards. I had to clean all the house, starting at the top and working down, sweeping and scrubbing right through. Heartstoning the steps from the front door to the pavement took me an hour alone. I was most conscientious. The meals I remember well. For breakfast I had bread and dripping. There were often mice dirts on the dripping to be scraped off first. Dinner was herring, every day; tea was bread and marge. I didn't have a bath during the month I was there, I wasn't given the opportunity; in fact there was no time to comb my hair properly, which was long – down to my waist; it grew so matted my mother had to cut off a lot of it when I finally came home again. My room was in the attic. There was a little bed in the corner, a wooden chair and a washstand. It was a cold, bare utterly cheerless room. (Lilian Westall, house maid in John Burnett (ed.), Useful Toil, Autobiographies of Working People from the 1820s to the 1920s (London, 1974), p. 216) Factory work contra domestic work USA When they (parents) went back (1914) I was thirteen and working here, so I begged my mother to leave me … My sister was eighteen and my brother sixteen. The woman who kept the boarding house said to my mother, ‘As long as they mind me, you don't have to worry. If they don't mind me I’ll write you and let you know.’ So my mother went back to Canada and we stayed here, but I’m the only one in my family who has always lived in Manchester. If I wanted to go back on the farm, I’d have to go work in private houses in Montreal and Quebec, take care of the house, wash the clothes, help with the food and whatever there is to be done. Housemaid.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".