"This is for fighting, this is for fun": popular Hollywood combat (war) films from the first Gulf war to the present (1990-2015)
Bibliographic record
Abstract
Hollywood has been making war movies since it began making movies. Widely credited as the first ‘Blockbuster,’ and one of the first films to establish Hollywood narrative techniques and conventions, D.W. Griffith’s 1915 film, Birth of A Nation, is an epic melodrama about the American Civil War ending with a literal marriage of the North and the South in the form of a young white heterosexual couple, solidifying the connection between war, families, and nation-building that has become the framework of the genre; hetero-nuclear families are the basis of the nation and war is a threat to these families, but ultimately also a critical component of nation-building/strengthening. These ideologies persist in contemporary combat films. The First Gulf War and those in Iraq and Afghanistan have had a major impact on this genre and this project investigates the (sometimes radical) shifts in representations of gender, sexuality, race/ethnicity, and nationality in popular Hollywood combat films made and released since the first Gulf War (1990) with a particular emphasis on more recent films (2005-2015) since these are the films which have received the least, if any, scholarly attention. Building on existing cultural, feminist, film, and postcolonial theory using a case study of selected popular Hollywood combat films and based primarily upon close textual analysis of the films themselves, this dissertation argues that these post-Cold War combat films are vital in creating and reinforcing cultural scripts about gender, sexuality, race/ethnicity, nationality, and war. This analysis adds to the field by identifying key cycles in the genre and arguing that, in fact, the ideologies of these films whether intentionally or not, reinforce the idea of a white, American, male-headed household as the norm to be protected, removing ‘Others’ from the frame, and implying that war is somehow natural, unending, and/or unavoidable, thereby creating a self-fulfilling prophesy wherein the more it happens, the more we seek to represent it, to gain mastery over it, the more natural and unavoidable it seems, and the more it continues to happen and seem normal and on and on into perpetuity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".