Self-Exculpatory Imaginings: Reenactment and Observation in <i>The Act of Killing</i>
Bibliographic record
Abstract
This article examines the contradictory inheritance of “observational cinema” on The Act of Killing (Oppenheimer, 2012): its recourse to direct cinema’s emphasis on portraiture; its reliance upon an image of personality derived almost exclusively from self-presentation; and the delegation of intentionality via technology and self-validating diegetic figures. Conjoined to its commitment to the historiographic significance of artifice and fantasy, The Act of Killing’s simultaneous – and paradoxical – affirmation of a behavioural spontaneity during its observational sequences is an equally important, if critically underappreciated, element in its moral demonstration. Initially lauded for exceeding the bounds of realism, this “observational documentary of the imagination” (Oppenheimer) also returns viewers to the more familiar individualized emotional geographies of direct cinema. In the final analysis, I argue, the tangled, unresolved exchange between self-consciously deployed reenactments and a more conventional observational approach in The Act of Killing ultimately discloses one source for the niggling political conundrums identified by the film’s critics.
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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 | 0.003 | 0.011 |
| 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 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".