Bibliographic record
Abstract
This paper examines fingerprint identification as a mode of state surveillance. Drawing on but critiquing the work of Simon Cole, it argues that the technique yielded a greater, more pervasive form of state surveillance by giving rise to new practices of data collection. This paper also highlights the photograph's role in fingerprint identification to argue for an essential transformation in law enforcement and surveillance practices announced by the intersection of fingerprinting and photography at the turn of the twentieth century. In contrast to traditional forms of visual surveillance, the collaboration of fingerprint identification and photography extended the surveillance gaze of the state in a manner often attributed to the rise of CCTV, enabling the state to bring all bodies – criminal and non-criminal alike – under surveillance. However, the unique capabilities afforded to the state through the intersection of fingerprint identification and photography remained largely theoretical until the advent of digital technologies in the 1960s and 1970s. At the start of the twenty-first century, advanced visual technologies and new media technologies reflect a restructuring of law enforcement and surveillance practices based on the aggregate collection of identification data. This paper argues that the continued photographing of fingerprints in contemporary law enforcement and state initiatives constitute heightened state surveillance and, as such, demands serious critical attention.
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.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".