Transforming<i>Transformers</i>into Militainment: Interrogating the DoD‐Hollywood Complex
Bibliographic record
Abstract
Abstract This article examines how the U.S. Department of Defense (DoD) and Hollywood collaborated to manufacture the blockbuster filmsTransformers(T) andTransformers: Revenge of the Fallen(TRF) to sell in global marketsandto sell a positive image of DoD personnel, policy, technology, and practice to the world.TandTRFare global militainment films made by the “DoD‐Hollywood complex” to make money in markets and put the U.S. military before the world in a positive light. To show how, the article's first section defines the “DoD‐Hollywood complex,” presents a brief 20th‐century history of its formation, and describes the current DoD institutions, policies, and practices that fuse DoD publicity agencies to Hollywood filmmakers. The second section highlightshowDoD assistedTandTRF's production and contemplateswhyHollywood solicited DoD support. The third section shows howTandTRFput DoD in a positive light. The conclusion addresses some of the consequences ofTandTRFwith regard to democratic theory. By showingTandTRFto be global militainment commodities, this article interrogates the nexus of “reel” and “real” U.S. military power and sheds light on how DoD interacts with Hollywood studios to influence how it gets screened by entertainment media and seen by global spectators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".