Bibliographic record
Abstract
Abstract Emotion characterizes the experience of film, as it does the experience of music. Because music almost always accompanies film, we may well ask what contributionmusic makes to the emotional aspects of film. The present chapter addresses this question. It should be said at the outset that in spite of the integral role of music for film, film music has been largely neglected by the disciplines of both musicology and music psychology until the last decade (e.g. Cohen 1994;Marks 1998; Prendergast 1991). The reasons for the neglect are complex, arising from social, technological, economic, historical, and cultural factors. Some of these factors also account for a parallel neglect by psychology of the study of film perception (Hochberg & Brooks 1996a, 1996b). Moreover, unlike other types of popular or art music, much music for film has been composed with the understanding that it will not be consciously attended to. Countering this neglect, the present chapter takes a psychological perspective on the sublime and remarkable emotional phenomena produced by music in the context of film. This chapter has the joint intent of supporting the argument that music is one of the strongest sources of emotion in film and of opening doors to further empirical work that explains why this is so.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".