Beyond the Implicit/Explicit Dichotomy: The Pragmatics of Plausible Deniability
Bibliographic record
Abstract
Abstract In everyday conversation, messages are often communicated indirectly, implicitly. Why do we seem to communicate so inefficiently? How speakers choose to express a message (modulating confidence, using less explicit formulations) has been proposed to impact how committed they will appear to be to its content. This commitment can be assessed in terms of accountability – is the speaker held accountable for what they communicated? – and deniability – can the speaker plausibly deny they intended to communicate it? We investigated two factors that may influence commitment to implicitly conveyed messages. In a preregistered online study, we tested the hypothesis that the degree of meaning strength (strongly or weakly communicated) and the level of meaning used by the speaker (an enrichment or a conversational implicature) modulate accountability and plausible deniability. Our results show that both meaning strength and level of meaning influence speaker accountability and plausible deniability. Participants perceived enrichments to be harder to deny than conversational implicatures, and strongly implied content as more difficult to deny than weakly implied content. Furthermore, participants held the speaker more accountable to content conveyed via an enrichment than to content conveyed via an implicature. These results corroborate previously found differences between levels of meaning (enrichment vs. implicature). They also highlight the largely understudied role of meaning strength as a cue to speaker commitment in communication.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".