Arresting Images: Mug Shots from the OPP Museum. PETERBOROUGH MUSEUM & ARCHIVES
Bibliographic record
Abstract
For the curious at heart, the Ontario Provincial Police (OPP) Museum's Arresting Images exhibition was a draw from the moment one read the title.Mounted at the Peterborough Museum & Archives (PMA), 1 the display, which primarily comprised historical mug shots of criminals, was a powerful evocation of an errant past.The PMA, a state-of-the-art facility, is well equipped to accommodate such an exhibition.A softly lit gallery space allowed for close inspection of the 100 or so expertly reproduced cartes-de-visite, 2 which were strategically placed at eye level around the room on three of the walls.The two sides of each carte-de-visite had been mounted inside matted frames, one side showing the photographic mug shot of the accused and the other the data recorded by the arresting officer.Thanks to the high quality of the reproductions, visitors had complete access to the details recorded on both sides of the cartes-de-visite.Secure centre-room display cases housed additional cabinet-card samples and vintage cameras of the period.The opportunity to view the faces of an assemblage of criminals holds a peculiar appeal.Though the legalities around protecting the individuals' privacy were not a concern, given that the cartes-de-visite are dated 1886-1908, the images were of a sort not normally accorded public scrutiny.Representing
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".