The Third Forensics - images and allusions
Bibliographic record
Abstract
The London Riots of August 2011 were notable for the prominence of closed-circuit television (CCTV) images of offenders in investigation and prosecution, and in social media and news publicity. The systematic use of CCTV footage in criminal investigations was not new, however. London's Metropolitan Police had pioneered specialist units tasked with acting upon image evidence in the five years prior to the riots, an approach deemed so effective it was termed the 'Third Forensics'. This article discusses the significance of this claim and its implications for the justice system. The use of images in the investigation of the riots was highly effective, suggesting claims for substantially improved impact in investigation and prosecution are valid, and earlier scepticism regarding both utility and surveillance society agendas in public area CCTV studies was justified. Systematic procedural use of CCTV footage is not new, however, as demonstrated following riots in Vancouver, Canada, and earlier in Bradford, UK. Furthermore, identification in the Third Forensics is eyewitness recognition, and not scientifically or technologically similar to fingerprints or DNA. The article suggests this difference affects risks of prejudice and miscarriages of justice, and profiling of individuals and social categories images appear to represent. The article concludes that while forensic investigation of CCTV images may not meet scientific criteria of a third forensic discipline, it defines nascent development in police investigation, where improvements in procedure have combined with proliferating CCTV systems and social media, leading to a novel set of circumstances raising a number of unexplored issues of such significance that 'Third Forensics' is a suitable term to use to symbolise them.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".