Visualizing War through Satellite Footage: Technological Capacity, Truth, and the View from Above
Bibliographic record
Abstract
Visualizing war is increasingly mediated through technology. Accompanying photographs from on the ground, media outlets often include satellite footage to offer context. Human rights organizations also rely on satellite imagery as a tool to confirm the veracity of images – as one example, Human Rights Watch used satellite footage to help confirm the authenticity of the Caesar images of Syrian torture victims. In the current context, where some critique the biased nature of media outlets, the infallibility of the photograph has perhaps been put into question. Satellite footage has become almost indispensable in response, as a tool to contextualize images and thus reinforce their positioning as authoritative. This contribution asks two key questions: first, how does satellite footage work in partnership with photographic imagery to invoke a sense of the real in media coverage of war? Second, how does the positioning from above affect the way we come to know war? Satellite footage changes the angle from which the viewer engages war, raising questions about how technological ways of seeing emerge, circulate, are framed, and function in wider narratives. Satellite imagery draws on the rhetoric of truth and, as noted, is widely used as a tool for human rights promotion and understanding of the realities of war. So how has satellite imagery come to function as a tool for ‘properly’ understanding war, how does this shift our understanding of what war is and what it looks like and how is this embedded in particular scopic regimes?
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".