Playing with our emotions: genre, realism and reflexivity in the films of Lars von Trier
Bibliographic record
Abstract
According to reviewers, bloggers, and scholars, Lars von Trier’s films, particularly Dancer in the Dark (2000) and Dogville (2003) tend to evoke multiple intense, often contradictory, emotional responses from viewers. The films’ dialectical effects can perhaps be explained by the fact that they broadcast their artifice, which results in a seeming break in the audience’s emotional immersion. The question that this thesis seeks to explore is how the films can simultaneously distance and engage viewers. Generic theories, as well as theories on emotion and film reception, are useful in exposing von Trier’s emotive strategies. In the end, it might be that von Trier endeavours to evoke emotions in viewers while also making us aware of his manipulations in order to suggest that as spectators we must constantly question the film and its creator. More troublingly, he implies that there might be something fundamentally perverse about our desire to watch films.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".