Strange Encounters: Exploring Law and Film in the Affective Register
Bibliographic record
Abstract
This paper argues that taking seriously the embodied and affective dimensions of thought is important in relation both to the critical and transformative possibilities of Law-and-Film scholarship. In it, the authors begin the work of revealing the ways that film works to produce what Raymond Williams called the 'structures of feeling' that help to cohere contemporary legal and political institutions. In its first section, it seeks to develop a more robust vocabulary for discussing how films work on their viewers. Building on the insights of William Connolly regarding the multilayered nature of thought, it discusses how the non-cognitive registers for thinking of technique, perception and affect are brought together in film. In the second section, the paper explores how these effects might be understood through a close reading of three short scenes drawn from the films The Piano (1993), Minority Report (2002) and Dead Man (1994). In the powerful contrast of 'affect' produced by each of these scenes (the latter two containing minimal narrative content), the authors make an argument for the significance of attending not only to the (fixed) representative or ideological dimension of film, but also to its movement, its flux and possibility as energy.
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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 | 0.006 |
| 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 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".