Functions of Conversation in Detective Fiction: An Analysis of Smiley’s Duplicate Keys From Grice’s Theory of Conversational Implicature
Bibliographic record
Abstract
Duplicate Keys , published in 1984, is a detective novel written by Pulitzer Laureate Jane Smiley. The novel develops around a murder discovered by Alice, the heroine, in Manhattan, in which two band members, Denny and his adopted brother Craig, were shot dead in Denny’s apartment. Since besides Alice, some of their other friends, and even their friends’ friends have duplicate keys, it’s extremely distracting and difficult for Police Detective Honey to solve the case. With suspense resolved and mystery unraveled, it turns out that the killer is Denny’s girlfriend and Alice’s best friend Susan, who pretends to be on a trip far away at the occurrence of the murder. The novel contains an abundance of conversations, which play a crucial part in plot advancement as well as characterization. Guided by Paul Grice’s theory of conversational implicature, the paper analyzes some conversations from Duplicate Keys , especially the disobedience of the cooperative principle in the conversations, deciphers the reasons behind the disobedience, while at the same time exposes characters’ inner world, and exhibits their personality traits. In so doing, functions of conversation in detective fiction are revealed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".