“This changes things”: Children, targeting, and the making of precision
Bibliographic record
Abstract
Avoidance of civilian casualties increasingly affects the political calculus of legitimacy in armed conflict. “Collateral damage” is a problem that can be managed through the material production of precision, but it is also the case that precision is a problem managed through the cultural production of collateral damage. Bearing decisively on popular perceptions of ethical conduct in recourse to political violence, childhood is an important site of meaning-making in this process. In pop culture, news dispatches, and social media, children, as quintessential innocents, figure prominently where the dire human consequences of imprecision are depicted. Children thus affect the practical “precision” of even the most advanced weapons, perhaps precluding a strike for their presence, potentially coloring it with their corpses. But who count as children, how, when, where, and why are not at all settled questions. Drawing insights from what the 2015 film, Eye in the Sky, reveals about a key social technology of governance we have already internalized, I explore how childhood is itself a terrain of engagement in the (un)making of precision.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".