A Multimodal Analysis of Negation in Princess Diana’s "Panorama" Interview
Bibliographic record
Abstract
Stylistic analyses of negation have traditionally and predominantly focused on linguistic texts due to lack of a well-defined tool for investigating negation in multimodal texts. To fill this methodological gap, the present study integrates the critical stylistics tool of negation in written texts with Kress and van Leeuwen’s (2006) framework of visual analysis to develop a tool for the analysis of negation in multimodal texts. This tool is named the multimodal textual conceptual function of negation (MTCFN) and is used to explore how multimodal meanings of negation are constructed in Princess Diana Panorama interview, broadcasted in 1995. The analysis revealed that the co-occurrence of language and images in the same text creates a co-text that regulates and determines the meanings of negation produced by both semiotic systems. The combination of the visual affordances of gaze direction, head tilts, and different shot types and angles helps reinforce and make coherent the meanings initiated through the verbal medium, thus creating a coherent and impactful multimodal narrative. The study concludes that stylistics holds significant potential for informing approaches to the analysis of multimodal texts and recommends that further research is carried out on other multimodal text types to test the explanatory adequacy of the proposed MTCFN tool.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".