Justicia’s Gaze: Surveillance, Evidence and the Criminal Trial
Bibliographic record
Abstract
This paper considers the use of the products of surveillance, primarily images, as evidence within the criminal trial. These products, whether static images, video or voice recordings, are increasingly being mediated for the fact-finder via ‘experts’, proffering an opinion about the meaning of some surveillance image, artefact or trace. Common law courts, including those in Australia, the UK, Canada, and the US, have been surprisingly accommodating towards such evidence—allowing incriminating opinions to be presented by witnesses with questionable or unsubstantiated, ‘expertise’. Institutional and judicial responses tend to be inattentive to the reliability of such evidence, and display a misplaced faith in the capacity of traditional trial safeguards to expose and manage the weaknesses inherent in this type of evidence. In looking at the ways in which courts use CCTV images, voice recordings and other traces generated by surveillant assemblages, this paper offers a legal site for consideration that has not featured as prominently in recent surveillance literature. It suggests that the preoccupations generated by the ubiquitous nature of everyday surveillance do not always map cleanly onto the use of surveillance artefacts (e.g. images and other traces) in the criminal justice system. At the same time this paper explores how ideas and concepts familiar to the analysis of surveillance techniques, cultures, imaginaries and practices might inform our understanding of the criminal trial and its related processes. Fundamentally concerned with the value of such evidence, this paper argues that, given the premium placed upon accuracy and fairness within the criminal trial, the state should be able to guarantee the basic trustworthiness of the opinions before interpretations derived from surveillance assemblages are admitted to assist with proof of identity and guilt.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".