Artificial intelligence's impact on legal journals challenges and opportunities for the Ottawa Law Review
Bibliographic record
Abstract
"This book provides an overview of the opportunities and challenges presented by the use of artificial intelligence (AI) and its impact on legal journals, notably the Ottawa Law Review (OLR). Throughout the publication lifecycle of a given piece, AI tools may play a pivotal role in enhancing the editorial and publishing processes. Similarly, authors submitting to legal journals may also leverage AI tools for purposes that range from improving readability to content generation. While the potential benefits are significant, the use of such tools raises various issues pertaining to the accuracy and quality of publications, as well as broader ethical and legal issues. Journals--both legal and non-legal--have responded to these opportunities and challenges at different speeds and in different ways. Some journals in non-legal disciplines have developed extensive AI policies, while the majority of legal journals--particularly in Canada--appear to be falling behind in this regard. This report contains several recommendations that will empower the OLR to embrace the transformative potential of AI responsibly while maintaining its commitment to safeguarding privacy, intellectual property, and scholarly rigour. Central to this endeavour is the adoption of three AI policies: one covering the use of generative AI and AI-assisted technologies in submissions, another addressing AI usage in the peer review process, and a final one relating to the editorial team. This report ultimately aspires to ensure the OLR upholds its reputation as a reliable contributor to legal scholarship by guiding the organization through this new technological revolution."--
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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 | 0.037 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.050 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".