The relevance of relevance: A review of Charles Forceville’s Visual and Multimodal Communication: Applying the Relevance Principle
Bibliographic record
Abstract
Relevance theory (RT), originally proposed by Sperber and Wilson (1986/1995), is a theory of human communication that is based on a general view of human cognition. In the classic version of RT, the paradigmatic example of human communication is a verbal exchange between a single speaker and a single hearer who are occupying the same space and time. In the book Visual and Multimodal Communication: Applying the Relevance Principle, Charles Forceville (2020) has two main goals: first, to make classic RT accessible to a wider (and non-expert) audience; and second, to adapt RT in order to account for other forms of communication beyond the purely verbal – namely, visual and multimodal communication. In the second half of the book, Forceville demonstrates how this adapted version of RT can be applied in a series of case studies on “static visuals”. In this extended review, I attempt to do justice to Forceville’s ambitious project and to outline the key concepts and terminology involved. In the discussion, I present some criticisms and questions, while offering some suggestions as to how adapted RT can be developed even further in order to account for moving images and film.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".