Make-Believe Empire: Imperial Intersections of the Film Military Industries at Ft. Ord, 1945-1970
Bibliographic record
Abstract
“Make-Believe Empire: Imperial Intersections of the Film & Military Industries at Ft. Ord, 1945-1970”Kelsey Kilgore Doctor of Philosophy Department of History,University of Toronto 2023 From 1940-1994, Ft. Ord, California, trained infantry soldiers using immersive kinetic exercises to recreate the places and peoples they would encounter upon deployment. In these exercises, soldiers imaginatively performed their combat roles with a sense of realism mediated by the visual lexicon of cinematography and Hollywood film. The U.S. Army, sometimes in collaboration with film industry professionals, constructed plywood sets, costumes, architecture, and other symbols of place to teach trainees how to see and know the enemy. A mock Vietnamese village at Ft. Ord built in 1966 was a wonder of applied cinematography. Its recreation of Vietnam drew on familiar visual tropes and stereotypes, and reinforced negative depictions of Asia rooted in WWII Hollywood film. It also reflected an intertwining of tourism, entertainment, and militarism that produced immersive recreations of place. After WWII, that knowledge was explicitly aimed at targeting the enemy for lethal force, which required psychological conditioning embedded in the pleasures of make-believe. In the village, trainees were shot twice, in training and then again as actors in publicly-distributed films about that training. The result was cohorts of trainees and civilian audiences who learned to understand Vietnam and the post-WWII U.S. Military through the lens of imaginative entertainment. This lens reinforced the film industry depiction of U.S. enemies as racialized subjects and legitimate targets of U.S. Military violence. This study uses an array of films, newspapers, reports, and ephemera from a now-closed base, to examine the adaptation of the visual lexicon of film and logics of tourism to combat training at Ft. Ord during the early to Cold War consensus period (1945-1970). A focus on its mock Vietnamese village (1966-1972), the largest west of the Rockies and public-facing until 1970, demonstrates adaptations of tourism and entertainment to military operation that trained soldiers and civilians alike to understand the global role of the U.S. in ways that benefited the military. The base’s adaptations of those intersections produced iterations of empire that were made and believed by soldiers in training and the audiences for whom they performed.
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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 | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".