Bibliographic record
Abstract
Frances Burney’s novel Camilla is an experiment in speculation. Charlatans and adepts in Camilla claim to be able to predict the futures of a cast of children, and Burney invites her readers to try, alongside these supposed experts, to predict the futures of these young people, whose economic, health, and educational futures are in flux. By reading Camilla in the context of popular fortune-telling games and probability theory, we can more clearly understand Burney’s use of the novel to critique various forms of projection. I examine in particular Every Lady’s Own Fortune-Teller, a 1791 manual that claimed to offer a new method of using scientific induction to tell individuals’ futures. Burney’s novel shows the danger inherent in this combination of scientific authority and reductive guesswork by demonstrating the varying effects of fortune-telling on two young characters: Camilla and her sister Eugenia. By simultaneously encouraging readers’ curiosity about the characters’ futures and undermining the efficacy and value of projection, Burney trains her readers to read more flexibly and to understand women’s lives in more complex, contingent ways.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".