Fear and Clothing: Dress in English Detective Fiction Between the First and Second World Wars
Bibliographic record
Abstract
This thesis addresses the anxieties of an ostensibly male readership of detective fiction between the Great War and Word War Two, through analysing dress. Based on a close reading of 261 texts chosen both from established and popular writers of detective fiction and from writers established in other literary, political and academic fields, this thesis establishes how concerns about class, gender and race are revealed through dress. It tracks the different dress mechanisms employed at the time to counter fear of post-World War One social and cultural turmoil, and assesses how effective those mechanisms were. The findings show that the dress strategies of both men and women changed in response to the effect of the Great War on masculinity, the effect of war and suffrage on performing womanhood and the approach of World War Two. Detective fiction was a comforting consolation literature, and this research demonstrates that the dress references provided further comfort through subtly offering the readership a guide to the dress codes of, primarily, the upper middle classes. The texts themselves could act not just to reflect anxieties, but to allay those anxieties by providing a form of conduct book for a confused, readership, to guide them through the insecurities of dress codes. This thesis thus increases academic knowledge on the power, materiality and usefulness of dress in fiction.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".