Novel Science or Oral History? The Admissibility of Co-Produced Information in Canadian Courts
Bibliographic record
Abstract
Co-production is an emerging source of information about the world, but it is one that has not been adequately theorized in the legal literature. Because co-production contains aspects of both novel science and oral history, it is not clear how it can be admitted. I argue that coproduced information does not clearly fit into either of the admissibility frameworks. With respect to the novel science framework, co-produced information fits into the criteria of testability, peer review, and standards with only a few problems, but would likely fail the general acceptance criterion of the test. However, if scientists are educated about co-production, or if it is possible to delineate a group of scientists who are more likely to accept co-production as the “relevant group,” then it may be possible for co-production to be admitted as evidence through the novel science framework. Turning to the oral history framework, co-produced information is less likely to be admitted because oral history is only a part, and not a necessary part, of co-produced information. As such, courts will likely be reluctant to bend the rules of evidence to admit it. Further research is needed to determine whether co-produced information can be admitted under the novel science framework.
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 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 | 0.061 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.044 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.019 | 0.014 |
| 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".