Bibliographic record
Abstract
I am much more hurt on your account than my own at your losing by this book; I hope it may yet sell: but if not, I have no judgment or St. Forlaix will make you amends; if I tho't it woud not I should be very unhappy. I am much obligd to your delicacy in not telling me this sooner, but you need never make any ceremony with me; for I am one who can hear truth tho' it makes against me. In any future publication I will take care you shall not lose; I will share the profit or loss of the history; & if, in any other, you disapprove that mode, we will not fix the price till what I write has been six months publish'd. I have thots of writing for the theatre, after the hist. is finishd; but it is difficult to get things done: if I succeed that way, I shall give up all others, as I like it best; in that case you know the price is always fix'd. (Frances Brooke to James Dodsley, 17??) BEYOND FEMINIST LITERARY HISTORY? Eschewing ceremony, able to hear the truth, negotiating future terms, liking the playwright's chances for success best – the writer of this 1769 letter to James Dodsley is clearly a competent literary professional, an economic agent confidently offering authorial expertise and flexibility as the basis for a durable and productive collaboration with this prominent bookseller.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.096 | 0.037 |
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".