Bibliographic record
Abstract
Decolonizing surveillance studies is an urgent task, needed to comprehend the unequal impacts of surveillance technologies in the past, present, and future. I discuss three aspects of research in comparison: technological novelty versus past experience, nation building versus colonization, and test versus initial operation of technology. Overall, I argue for the significance of colonial narratives that illustrate the early and severe, often violent experiences of surveillance that tend to be historically underestimated or politically concealed. First, scholarly work has been attracted to technological novelties of digital surveillance. But to grasp the social implications of surveillance, the historical background of technology offers a genealogical thread, and the past awaits as a rich repository to be discovered. Second, previous studies have drawn plural origins of modern surveillance from Western civilization. But modern nation building and colonialism should be examined together in research, rather than separating them and placing nation building as central to modernization while placing colonialism as a side effect or exception of modernization. Such a separation fails to grasp the experiences of modern surveillance as a whole because nation-state is all too often colonial nation-state. Lastly, I question the prevailing concept of a “boomerang effect,” meaning that Western countries first test out harmful techniques on colonies, but soon these techniques come home. This boomerang effect view centers on the West. A “test” of surveillance technology targeted a group of people for its own purpose, and the systematic practice of surveillance left irreversible effects in colonies. Those effects immensely contributed to today’s foundation of global political economy as an ongoing process of technological dominance of the Global North over the South. To decolonize surveillance studies, it would be better to discuss the global experiences of surveillance in the frame of unequal distribution and outcome of technology.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.093 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.008 |
| 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".