Bibliographic record
Abstract
Critics have frequently taken Martin Amis to task for the apparent relish with which he depicts his misogynistic characters’ predilections, which range from pornography and erotic underwear to rape, as well as his portrayal of female characters as the passive victims of male violence. London Fields (1989) raises both these issues, featuring a female murder victim or “murderee,” Nicola Six, who stars in her own pornographic films and uses an extensive collection of “wondrous frillies and costliest scanties” (321) to fulfill the fantasies of the hapless grotesque, Keith Talent. 1 Critical discussion about the novel’s gender politics has focused overwhelmingly on issues of power and powerlessness. Amis and other critics have defended Nicola against charges of passivity and victimization by claiming that she controls the male characters: she not only sleeps with but also manipulates working-class Keith, aristocratic Guy Clinch, and Samson Young, the narrator. This essay will argue that power and powerlessness have been invoked in problematic ways in discussions of London Fields , and in feminist analysis more generally. I seek to refocus the debate about gender and London Fields by arguing that the symbolic importance of femininity is primarily related to questions of form and authorship. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".