The Admissibility of Social MediaEvidence in CanadaPart 2: The Canada Evidence Act
Bibliographic record
Abstract
In Part 1 of this column, I reviewed the law concerning the admissibility of social media evidence in Canada at common law. In this second part, I will consider its admissibility pursuant to the Canada Evidence Act. Reprinted in R.S.C. 1985, c. C-5. As noted in Part 1, the issue of authentication of social media evidence has been addressed by Canadian judges through both the common law and the Canada Evidence Act (Act). In R. v. Hirsch, it was suggested that the provisions in the Canada Evidence Act dealing with the admissibility of social media evidence “is a codification of the common law rule of evidence authentication.” [2017] SKCA 14 at para. 18. This is true, but as will be seen, the statutory provisions are much broader than that, including how an “electronic document” is defined.
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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 | 0.028 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".