Bibliographic record
Abstract
Climate change is the great disrupter of humanity, and the Canadian legal profession is at an inflection point. This article begins by briefly outlining the self-governing legal profession’s duties in Canada to uphold and protect the public interest in the administration of justice, including ensuring competencies. It then chronicles, and engages in a comparative analysis of, climate change-related resolutions and actions taken across 15 legal bars, societies, and associations around the world and situates those actions to current measures in Canada. In addressing some of the barriers found within self-regulatory bodies and voluntary associations for Canadian lawyers, the article then provides a basic primer for lawyers to understand the growing significance of climate change impacts on legal systems and civilizations and its potential to undermine legal rights. It identifies further areas of research that are needed regarding legal competencies in understanding climate-related risks and opportunities in relation to lawyers’ duty of care to their clients in Canada. Rapid developments in the law and evolving risk registers in response to a warming world are creating new understandings of what constitutes climate competent lawyering. This article lays the groundwork for further work in climate-related actions for the Canadian legal profession.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".