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. Résumé 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. Mots-clés: Drones; Surveillance; Humanités numériques; études post-coloniales; Mondialisation; Cultures numériques
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".