Seeing and Unmaking Civilians in Afghanistan: Visual Technologies and Contested Professional Visions
Bibliographic record
Abstract
This article examines the politics of 'seeing' civilians in Afghanistan with a focus on the 2009 Kunduz air strike. Drawing on the literature on professional vision and professional knowledges, I ask how divergences in the 'ways of seeing' between different professional communities can be explained, and how they are resolved in practice. 'Seeing,' I argue, is based on talking. The vocabularies with which we describe the world and understand our relationships shape how we 'see'. As a consequence, Afghans gathered around a truck can appear an 'immediate threat' or not -- depending on the ideological prisms at work. The article suggests that we need to treat professional vision as necessarily contested and examine how professionals are socialized into accepting one way of seeing as valid. Seeing is based on talking, and we need to talk about how we see (violence).
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.062 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".