Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Women are occasionally governors of prisons for women, overseers of the poor, and parish clerks. A woman may be ranger of a park; a woman can take part in the government of a great empire by buying East India Stock. — Barbara Bodichon, A Brief Summary in Plain Language, of the Most Important Laws Concerning Women (1854) O N OCTOBER 5, 1860, GEORGE HENRY LEWES VISITED a solicitor in London to consult about investments. He wrote in his journal: “[The Solicitor] took me to a stockbroker, who undertook to purchase 95 shares in the Great Indian Peninsular Railway for Polly. For £1825 she gets £1900 worth of stock guaranteed 5%” (qtd. in Ashton, Lewes 210). Thus Marian Evans, called Polly by her close friends, known in society as Mrs. Lewes and to her reading public as George Eliot, became a shareholder in British India. Whether or not Eliot thought of buying stock as taking part in the government of a great empire, as her friend Barbara Bodichon had written in 1854, the 5% return on her investment was a welcome supplement to the income she had been earning from her fiction since 1857. From 1860 until her death in 1880, she was one of a select but growing number of middle-class investors who took advantage of high-yield colonial stocks. 1 Lewes’s journals for 1860–1878 and Eliot’s diaries for 1879–80 list dividends from stocks in Australia, South Africa, India, and Canada. These include: New South Wales, Victoria, Cape of Good Hope, Cape Town Rail, Colonial Bank, Oriental Bank, Scottish Australian, Great Indian Peninsula, Madras. The Indian and colonial stocks make up just less than half of the total holdings. Other stocks connected to colonial trade (East and West India Docks, London Docks), domestic stocks (the Consols, Regents Canal), and foreign investments (Buenos Aires, Pittsburgh and Ft. Wayne) complete the portfolio. 2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it