Bibliographic record
Abstract
Relevance Theory has been dismissed as inapplicable to literary genres first for its failure to come up with interesting and plausible new interpretations and second for its demonstrations’ depending on contexts that are personal and immediate compared to the timeless public stages of the literary utterance. The first complaint is easily addressed: RT is not a hermeneutic tool; the laboriousness and banality of putative interpretations are not evidence in themselves of RT’s incapacity to explain the inferential conditions of literary genres. The second complaint is addressed first by revisiting the typical demonstration of Relevance principles and finding that interlocutors’ implicature-generating indirectness is a communicative efficiency insofar as it enriches shared consciousness. The ostensive-inferential model is then taken towards the literary field by first demonstrating its working in a (simple) expression in visual art. Beyond these ground-level demonstrations, the chapter recognises problems with RT’s relying on Grice’s ‘intended meaning’ – for intention comes up against the ‘intentional fallacy’ in literary appreciation. Suggesting that a more pragmatic sense of intention could ease some fallacy fears, the chapter brings to the table Taylor’s reminder of Grice’s distinction between natural and non-natural meaning, and, further, proposes that the inferential structure of literary genres produces a depth of field so fertile that it may be a clue to if not strict evidence of Sperber and Wilson’s ‘dedicated module’. The chapter concludes by proposing intertextuality and, in addition, indirectness itself as projects for exploratory applications of RT to literary utterance, and then offers three very brief case studies, from Austen, Zola, and Dreiser.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".