Building Spectatorial Solidarity against the “War on Terror” Media-Military Gaze
Bibliographic record
Abstract
At the dawn of the 21st century the “War on Terror” ushered in an era in which some were besieged by wars and others by war-related imagery. For the fortunate who live outside of war zones, mostly in the Global North and West, the experience of war has been primarily a mediated one. With the advent of digital imagery and its many evolving and developing technological transmutations, the possibilities of reproduction, representation, manipulation, and circulation have grown exponentially in the past twenty years. Yet in the grand scheme of human communication history, the “pictorial turn” is a relatively recent phenomenon that requires further analysis. In this article, I unpack and analyze some of the key media moments from the vast visual lexicon and iconography of the “War on Terror” to reveal its scaffolding and machinations and offer counterstrategies of resistance. I argue that the “War on Terror” is the orchestrated sum of literal and figurative imagery, a coordinated public relations disinformation media campaign designed to hide real wars and their true destruction and costs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".