Peacekeeping or Expeditionism: Identity and Ethics Among Canadian Army Drone Operators
Bibliographic record
Abstract
ABSTRACT Small and middle states such as Canada have been integrating drones into their militaries for over 20 years, but their drone use has been understudied. Delving into the data from interviews with 33 army drone operators, this paper proposes two arguments about how drone operators negotiate their identity‐based reflections on their roles. First, the paper argues that drone operators in the Canadian army center their identity on the idea that they are part of the combat arms and construct their role as drone operators through this lens. Second, this paper argues that drone operators connect this combat arms identity to broader ideals of what the Canadian Armed Forces (CAF) should do and be: a peacekeeping or an expeditionary force. In turn, ideals of what the CAF should do and be affect how drone operators think about drone use. On the one hand, those who viewed the CAF as a helping, peacekeeping military argued for limited and unarmed drone use, whereas those who encouraged the expeditionary elements of the CAF wanted weaponized and further integrated use of drones. These complex reflections from drone operators themselves are important for understanding how emerging technologies are thought of by their users in military contexts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".