The Art of the Unsaid: Analyzing the Use of Conversational Implicature in Political Communication
Bibliographic record
Abstract
This study investigates the use of conversational implicature by Saudi Crown Prince Mohammed bin Salman (MBS) during a September 2023 interview with Fox News. The research is grounded in Grice’s conversational maxims and the theory of implicature, focusing on how conversational implicature is strategically utilized to navigate sensitive topics and influence public perception. Using a qualitative research design grounded in Grice’s theory of implicature, the analysis highlights the frequent flouting of conversational maxims—particularly quantity, relation, and manner—to avoid direct responses, reframe controversial questions, and maintain diplomatic flexibility. The findings indicate the frequent use of particularized conversational implicatures (PCI) tied to specific geopolitical contexts, where MBS relies on context to imply meaning without making explicit statements. Conversely, generalized conversational implicatures (GCIs) were observed in broader discussions on Saudi Arabia's military and economic strategies, where meaning is naturally inferred without dependence on specific contexts. The findings suggest that MBS effectively employs implicatures to manage multiple audiences, deflect criticism, and shape public perception. Future research could expand on these findings by analyzing a wider range of interviews and incorporating non-verbal cues.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| 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".