Bibliographic record
Abstract
How is gendered language utilised to position the United States in relation to target states to morally justify Remotely Piloted Aircraft (RPA) strikes? State discourse of the US during the George W. Bush and Barrack Obama administrations projected an image of remotely piloted systems as mechanisms of masculine protection. US officials assert that RPAs not only protected Americans at home, they protected populations vulnerable to terrorist attack abroad. While the RPA itself was coded as masculine, RPA pilots are feminised because they are protected from battle while using the RPA. The RPA takes the position of the ultimate masculine protector and its operators become feminised in US rhetoric. The surveillant assemblage of pilot, RPA, and sensor-analytics systems sustaining the RPA, is examined through a rigorous discourse analysis of state officials’ statements during the Bush and Obama administrations. Statements are taken from a number of reputable publications including The New York Times, The New Yorker, The Atlantic, Al Jazeera, CNN, and BCC, among others. Statements are also taken from the report “Living Under Drones,” from the law schools of Stanford and New York University. This research begins to answer the question of how technology is gendered in relation to RPAs and RPA strikes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".