âOcular Proofâ: Photographs as Legal Evidence
Bibliographic record
Abstract
This article traces the rules governing the admissibility of photographs into evidence in the courts of law of Canada, the United States, and Britain. The discussion is grounded in the nineteenth-century discourse of photographic objectivity, and examples of case law and legislation are cited to show the evolving response of the courts. The legal understanding of photographic evidence is examined from the late nineteenth century onward, and the twenty-first-century concerns surrounding the authenticity and admissibility of digital photographs are highlighted. RÉSUMÉ Ce texte retrace les règlements qui gouvernent l’admissibilité des photographies en tant que preuves dans les cours de justice au Canada, aux États-Unis et en Grande-Bretagne. L’auteur base son analyse sur le discours de l’objectivité photographique du XIXe siècle, et des exemples de jurisprudence et de législation sont utilisés pour montrer l’évolution de la réponse des tribunaux. Il examine la valeur légale de la preuve photographique à partir de la fin du XIXe siècle jusqu’à présent, en soulignant les préoccupations du XXIe siècle par rapport à l’authenticité et à l’admissibilité de la photographie numérique.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".