DIFFICULTIES WITH CRITICS * Kingsley Amis: Antimodels and the Audience. By ANDREW JAMES. McGill-Queen's University Press, 2013; $100
Bibliographic record
Abstract
This ambitious and not always successful book raises questions about how literary criticism can be most usefully conducted, especially when its subject is the novel. Andrew James has not only read all of Kingsley Amis's fiction, but seemingly every essay and review provoked by it. He has also read Amis's thesis, submitted and rejected for the BLitt degree at Oxford. He has read Amis's first, unpublished novel, The Legacy; also the version of Difficulties with Girls that Amis broke off and only returned to after some years. He has consulted the manuscripts of Amis's work in the Huntington Library and provided us with passages from a number of them. Amis couldn't have found a more sympathetic, hard-working devotee of his books. The troublesome question I found myself confronted with as I read the book concerned the relation – or lack of it – between Mr James's critical commentary and the immense readerly satisfactions Amis's novels offer. What is the best way to bring these out for readers looking for helpful commentary on books that have afforded them much pleasure?
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".