Reassessing the Explicit-Implicit Distinction: A Critical Analysis of Gricean Pragmatics and Relevance Theory
Bibliographic record
Abstract
This paper investigates the problematic nature of the explicit-implicit distinction in the theory of meaning. It reviews how this notion has been addressed in the two major theories of the inferential approach, namely, the Gricean semantics-pragmatics theory and Wilson and Sperber’s Relevance Theory (RT). The representation of meaning in terms of the explicit-implicit distinction is attributed to Gricean semantics-pragmatics, which is operationalised in terms of truth-conditions for the explicit meaning and implicatures for the implicit meaning. Although Gricean proposals have laid the foundations for most of the work in the theory of meaning, the paper highlights that such an explicit-implicit distinction cannot be a clear-cut division and identifies some irregularities in the interplay between Grice’s linguistic meaning and conversational implicatures. The paper then re-analyses Gricean pragmatics in the light of Relevance Theory (RT) and concludes by explaining the salient issues with the clear-cut distinction between explicit and implicit meaning. The study finds that Grice’s view—that the semantics-pragmatics distinction is based on the saying/implicating distinction—does not hold because conversational maxims play a significant role in reference assignment and in determining which of the logically possible senses of what is said the speaker could have intended.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.010 | 0.037 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".