Bibliographic record
Abstract
This article examines how the global traumas of resource-driven conflicts and acts of terrorism are mapped in 21st-century US and UK narrative cinema, and suggests that guilt, elicited in the implied Western viewer, is displaced in the films onto images of Western women. Revisiting Mulvey’s influential theory of ‘visual pleasure’ through the ‘male gaze’, this article analyses the films Traffic (2000), a depiction of US complicity with global drug cartels, Babel (2006), the story of a global media frenzy surrounding American tourists victimized in Morocco, and three films about crises in Africa: Shooting Dogs (2005), a dramatization of Western apathy during the 1994 Rwandan genocide, The Constant Gardener (2005), about pharmaceutical testing in Kenya, and Lord of War (2005), based upon the life of an arms dealer. A theoretical re-engagement with feminist film theory is followed by analyses of the films to illustrate how the guilt elicited by each of the films’ traumatic contexts conjoins with the primal psychological experience of lack. Viewers’ and their screen surrogates’ combined sense of helplessness in the face of others’ trauma is displaced ‘hysterically’ onto images of women, exposing a troubling new looking relation in our traumatic age.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".