Urban Surveillance and Panopticism: will we recognize the facial recognition society?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the implementation of facial recognition surveillance mechanisms as a reaction to perceptions of insecurity in urban spaces. Facial recognition systems are part of an attempt to reduce insecurity through knowledge and vision, but, paradoxically, their use may add to insecurity by transforming society in unanticipated directions. Facial recognition promises to bring the disciplinary power of panoptic surveillance envisioned by Bentham - and then examined by Foucault - into the contemporary urban environment. The potential of facial recognition systems – the seamless integration of linked databases of human images and the automated digital recollection of the past – will necessarily alter societal conceptions of privacy as well as the dynamics of individual and group interactions in public space. More strikingly, psychological theory linked to facial recognition technology holds the potential to breach a final frontier of surveillance, enabling attempts to read the minds of those under its gaze by analyzing the flickers of involuntary microexpressions that cross their faces and betray their emotions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it