CLAUDIA L. JOHNSON and CLARA TUITE (eds), A Companion to Jane Austen.
Bibliographic record
Abstract
CLAUDIA L. JOHNSON and Clara Tuite’s introduction to this wide-ranging collection of essays focuses on what they identify as a contemporary ‘reenchantment’ with Austen, in distinction to a ‘revival’, which would, they note, be somewhat inappropriate ‘in connection with a figure whose vitality has never abated’ (p. 1). It is indeed an astute characterization, as the sense of new possibilities shapes the pieces that constitute this thought-provoking and engaging book, as does the inevitable awe at the powerful sorcery through which this author managed to transfigure her little bit of ivory into a key that could open doors into so many wondrous expanses. There must have been some editorial magic as well in bringing together forty-two essays by critics from the United Kingdom, the United States, Australia, and Canada (and one from Italy), encompassing topics from the nineteenth-century illustrations of the novels (Laura Carroll and John Wiltshire), to ‘The Gothic Austen’ (Nancy Armstrong), Austen’s representations of the military (Gillian Russell), Austen and commodification (Barbara M. Benedict), word games and Emma (Linda Bree), music in Austen’s world (Gillen D’Arcy Wood), and ‘Austenian subcultures’ (Mary Ann O’Farrell), into a single volume continuing Blackwell’s extensive ‘Companion’ series.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.039 |
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".