The Ecology of Listening while Looking in the Cinema: Reflective audioviewing in Gus Van Sant's<i>Elephant</i>
Bibliographic record
Abstract
This article argues that the state of spatial awareness engendered by the art of soundscape composition can be productively extended to the act of listening while looking in the cinema. Central to my argument is how Katharine Norman's concept ofreflective listeningin soundscape composition can be adapted toreflective audioviewingin the audiovisual context of film. Norman begins the process of intersecting film theory and the discourse of soundscape composition by appealing to famed Soviet filmmaker Sergei Eisenstein's theories of montage to illustrate how soundscape composition enables active listener engagement. I extend her discussion of Eisenstein to demonstrate how this filmmaker's thinking about sound/image synchronisation in the cinema – and R. Murray Schafer's own predilection for Eisensteinian dialectics – can be understood as a means towards the practice of reflective audioviewing. I illustrate my argument with an analysis of how the soundscape compositions of Hildegard Westerkamp have been incorporated into Gus Van Sant's filmElephant. Attention to the reflective qualities of Westerkamp's work open up new dimensions in our experience of the audiovisual construction of space in the film. Ultimately I argue that the reflective audioviewing prompted byElephantcan be carried into considerations of all films that make use of sound design for spatial representation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".