© The author(s), 2012 | Licensed to the Surveillance Studies Network under a Creative Commons Attribution Non-Commercial No Derivatives license.
Bibliographic record
Abstract
Sometime near 2007 the world reached a ‘watershed in human history’- the global urban population surpassed the rural (Davis 2007). Distinct from intimate towns and sprawling suburbs, cities are filled with strangers living in close proximity to one another (Jacobs 1961). Surveillance is a traditional element, perhaps even a defining feature of urban life (Coaffee et al. 2009): thus, surveillance is increasingly urban surveillance. In 21st Century cities, this surveillance is intensifying and mutating as the strangeness and anonymity of urban life is either fading or adopting new forms. This issue is about the new forms, arrangements, and representations of urban surveillance. In the emergent highly-developed ‘cybercities’, where digital technologies and urban life converge (Graham 2001), surveillance is becoming more concentrated, hidden, passive, functional, mobile, and varied (Lyon 2007). If camera surveillance is often considered the prototype of surveillance (Doyle et al. 2011), then emblematic of growing mobility and mutations of urban surveillance is the ever-moving, police-controlled cameras and recent linking of public and private camera surveillance systems (such as in Chicago’s city centre (ACLU of Illinois 2011); the recent proliferation of cell phone camera use and image transfer by average citizens to document and publicize police brutality (see: Finn 2011) such as during the G20 protests in Toronto; and similar use of mobile camera phones and hand-held cameras by
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.485 | 0.260 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".