When You Play The Game of Drones, You Win or You Die: Examining the Role of U.S. Drone Strikes in U.S. and English Language Allies Newspapers from 2008-2019
Bibliographic record
Abstract
In the years following the terrorist attacks on September 11, 2001, the United States ramped up its usage of drones and drone strikes around the world. Spanning three United States’ presidents, drone strikes became a regular feature in the US military arsenal. While American newspaper media and citizens have been very pro-drone, global citizens view drones in a far more negative light. This study examines US military drone strikes and English-speaking allied newspapers in Australia, Canada, New Zealand, and the United Kingdom and evaluates if coverage remains positive or negative depending on the newspaper’s conservative or liberal leanings from 2008–2019. The argument was tested by using a qualitative research methods methodology using a case study approach and newspaper content analysis with the theory of smart power. The results of this study found that the political leanings in our English-speaking allies’ newspapers do have some influence if the articles are positive or negative towards US drone strikes, but it is not a one size fits all situation. American newspapers, the liberal New York Times and the conservative Wall Street Journal, remained positive towards US drone strikes throughout the years of this study. Interestingly, this study also found that as the years of war continued, the number of drone strike articles found in the US and our English-speaking allied newspapers decreased, perhaps reflecting a donor fatigue situation.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".