“The mood is in the shot”: the challenge of moving-image texts to multimodality
Bibliographic record
Abstract
This article reports a longitudinal study of new media and digital technologies producers (Rowsell 2013) looking at their multimodal logic and practices to challenge notions of text and multimodality. Focusing on filmmakers, I build on previous research (Sheridan and Rowsell 2010) to extend traditional notions of print-based texts to more contemporary ways of making meaning with moving-image texts. Working within a multimodal framework (Kress 1997, 2010), I present the logic and practices of two producers. One filmmaker produces documentaries about wide-ranging topics from cricket to Jim Carrey to sex scandals and religion. The other producer creates 3-D animated “texts” for film and television. Both are assiduous about their process and product, both highly competent at editing filmic texts, both intimately acquainted with the art and logic of multimodality. Their production stories and expertise inform the article to challenge perceptions of what modes can do and what they can evoke. Whether it is done through expressions, movements, images, sounds, filmmakers exploit the affordances of modes to emotionalize moving-image texts.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".