Technology vs ‘terrorism’: circuits of city surveillance since September 11th
Bibliographic record
Abstract
Since September 11th 2001 ‘terrorism’ has understandably become the preoccupation of many, especially in urban areas, where the threat of ‘terrorism’ is greatest. High on the list of priorities is tightening up the technological means of ensuring security, by adopting in particular new surveillance measures. While these are mainly expansions of already existing systems — biometrics, ID cards, CCTV and communications interception — an interesting and perhaps disturbing new feature of these is the apparent willingness to create modes of integration between previously separate systems. Similar software and dependence on algorithmic techniques permit data‐sharing across several boundaries that were previously less porous. The dispersed data‐gathering of the surveillant assemblage, that includes relatively ‘innocent’ items such as consumer transaction trails —‘categorical seduction’— converges with the more centralized activities of policing and intelligence —‘categorical suspicion’— in the effort to make urban areas safe. The consequences of this are likely to be far‐reaching, reinforcing our reliance on technological solutions, and increasingly inserting them into the routines of everyday life in the city. Depuis le 11 septembre 2001, le ‘terrorisme’ est naturellement devenu la préoccupation de beaucoup, surtout dans les zones urbaines où la menace ‘terroriste’ est la plus forte. Aux premiers rangs des priorités, on trouve les moyens technologiques d'assurer la sécurité, notamment l'adoption de nouvelles mesures de surveillance. Si certaines consistent principalement àétendre les systèmes existants (biométrie, cartes d'identité, circuits de télévision et interception des communications), l'une des nouvelles méthodes, intéressante mais quelque peu troublante, est la volonté apparente de créer des modes d'intégration entre des systèmes jusqu'alors indépendants. Des logiciels similaires et une subordination à des techniques algorithmiques permettent le partage de données à travers plusieurs frontières auparavant moins perméables. La collecte de données éparses dans l'assemblage de surveillance, incluant des éléments relativement ‘innocents’ tels que le suivi des transactions de clients s'allie aux activités les plus centralisées de la police et du renseignement afin de sécuriser les zones urbaines. Les conséquences sont susceptibles d'aller plus loin, renforçant notre dépendance à l'égard de solutions technologiques et multipliant celles‐ci dans les routines de la vie quotidienne urbaine.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".