The Canadian Armed Forces “YouTube War”: A Cross-Border Military-Social Media Complex
Bibliographic record
Abstract
The goal of this paper is to conceptualize, contextualize, and critically analyze the Canadian Armed Forces’ (CAF) use of YouTube to promote itself, recruit soldiers, and frame its role in the post-9/11 U.S.-led NATO war in Afghanistan. The first section of this paper engages with scholarship on war and the media, the military-industrial-communications complex (MICC), and YouTube War to conceptualize YouTube as a tool and contested battle-space of 21st century new media wars. The second section contextualizes the rise of the CAF’s YouTube channels—Canadian Forces and Canadian Army—with regard to post-9/11 Canadian foreign policy, the growth of the Canadian military publicity state, the creeping militarization of culture, and the CAF’s “social media policy”. The third section conceptualizes the CAF’s two YouTube channels as tools and spaces of its publicity front; then, through a synoptic critical overview of numerous CAF-generated YouTube videos, it shows how the CAF uses YouTube to recruit personnel and frame its role in the war in Afghanistan. The conclusion discusses the characteristics of this cross-border military-social media complex and its contradictions, namely, the spread of pacifist and veteran-generated videos that contest the war in Afghanistan. Overall, the paper offers an initial political-economy of communication of the CAF’s foray into the global battle-space of the Internet and its use of YouTube for publicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".