Bibliographic record
Abstract
This article investigates vaporwar – a kind of military fanvid where military footage is remixed, set to music and given a ‘vaporwave’ makeover through filters and editing. While a niche group, it is a growing one and loved by its fans. Why? Drawing on Turk and Johnson’s idea of looking at vidding as an ecology, this article suggests that these videos should be seen as part of a ‘contents fandom’ of the military. This term is adapted from ‘contents tourism’, where people travel to a location due to its association with different media depictions of the same subject, and here means that the fans who make and view these videos do so out of a fannish interest in all things military. These videos are thus part of a turn towards ‘participatory militainment’, media made by military fans for military fans, drawing on the internet vernacular of vaporwave to prove the genre’s coolness and validity. At the same time, they point to a sense of nostalgia and dissatisfaction in this fandom with current military trends, and an aestheticization of the military as a reason for its existence. What does it mean for the military to have fans?
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".