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Record W6921106091 · doi:10.6093/2035-8504/8571

Relevance Theory for Fiction

2021· article· en· W6921106091 on OpenAlexaff

Bibliographic record

VenueUniversità degli Studi di Napoli Federico II · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEthics, Aesthetics, and Art
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRelevance theoryRelevance (law)ComplaintFallacyField (mathematics)Natural (archaeology)Literary scienceLiterary criticism

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.236
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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