“The mood is in the shot”: the challenge of moving-image texts to multimodality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it