Social Incrimination: How North American Courts are Embracing Social Network Evidence in Criminal and Civil Trials
Bibliographic record
Abstract
fter the 2011 Stanley Cup finals in Vancouver, infuriated citizens turned en masse to Facebook to search for culprits responsible for vandalizing cars and looting downtown stores.After a bank robbery in Texas, police arrested two bank tellers, the boyfriend of one and the brother of another, after being informed that one of the four posted, "IM RICH" on Facebook. 1 In New Brunswick, a defendant in a person injury lawsuit persuaded the judge to force the plaintiff to archive her Facebook account after photos of her zip-lining appeared to demonstrate that her injuries were not as serious as she claimed. 2 Interesting stories like this surface almost every day.Incriminating statements made on a social network may end up opening the door to more substantial investigations.Sometimes investigators discover the evidence, although often it is an individual's own online "friends".This is B.A. (
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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 | 0.076 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.044 | 0.046 |
| Scholarly communication | 0.035 | 0.024 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".