The Admissibility of Social MediaEvidence in Canada:Part 1
Bibliographic record
Abstract
Any trials arising out of the events in Washington, D.C., on Wednesday, January 6, 2021, are likely to result in attempts to introduce what has been described in Canada as “social media evidence,” i.e., text messages, Facebook postings, selfies, etc. (see Lisa A. Silver, The Unclear Picture of Social Media Evidence, MANITOBA L. J., 43, no. 3 (2020) at 111). Professor Silver has noted that “social media is often the context in which criminal offences can be committed. It can provide a space in which offences are committed and it can provide proof of it as well” (at 117-118). Interestingly, social media evidence is often viewed as extremely reliable because it is presented in a manner in which judges can hold, see, and review on their own. Like other documentary evidence, it can be viewed as superior to testimonial evidence, though it is subject to many of the same inherent frailties (see Scott v. Harris, 550 U.S. 372 (2007), for an example of how viewing the same video recording led numerous judges to different conclusions).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".