Bibliographic record
Abstract
Digital technologies are essential to establishing new forms of dominance through drones and surveillance systems; these forms have significant effects on individuality, privacy, democracy, and American foreign policy; and popular culture registers how the uses of drone technologies for aesthetic, educational, and governmental purposes raise questions about the exercise of individual, governmental, and social power. By extending computational methodologies in the digital humanities like macroanalysis and distant reading in the context of drones and surveillance, this article demonstrates how drone technologies alter established notions of war and peace, guilt and innocence, privacy and the common good; in doing so, the paper connects postcolonial studies to the digital humanities. <strong>Résumé</strong> Les technologies numériques sont essentielles pour établir de nouvelles formes de domination par le biais des drones et des systèmes de surveillance. Ces formes ont des effets importants sur l’individualité, la vie privée, la démocratie et la politique étrangère américaine. La culture populaire dénombre un éventail de ces effets employant des technologies de drones pour des objectifs esthétiques, éducatifs et gouvernementaux d’une manière qui soulève des questions sur la mise en pratique du pouvoir individuel, gouvernemental et social. En étendant des méthodologies statistiques des Humanités numériques, tels que la macroanalyse et la lecture globale, dans le contexte des drones et de la surveillance, cet article démontre la façon dont les technologies numériques modifient fondamentalement les notions déjà établies de la guerre et de la paix, de la culpabilité et de l’innocence, de la vie privée et du bien commun. De ce fait, cet article lie les études post-coloniales aux Humanités numériques. <strong><br /></strong> <strong>Mots-clés:</strong> Drones; Surveillance; Humanités numériques; études post-coloniales; Mondialisation; Cultures numériques
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".