Putting Climate Change Risk on the Boardroom Table- A Conversation with Carol Hansell and Gigi Dawe
Bibliographic record
Abstract
Corporate board members are legally obligated to address climate change risk and opportunities as part of their oversight of the companies they serve, according to a new, in-depth legal analysis of directors’ duties regarding climate change risk. In her opinion, titled “Putting Climate Change Risk on the Boardroom Table” Ms. Hansell clarifies the role of the corporate board with respect to climate change and is unequivocal about the responsibility of corporate directors to include climate change risks and opportunities in their oversight and strategic direction of the companies they serve. Ms. Hansell’s ground-breaking opinion, given to the Canada Climate Law Initiative, a research hub at the University of British Columbia Allard School of Law and York University Osgoode Hall Law School, is the first in-depth legal analysis of directors’ duties in a corporate governance context by a senior Canadian lawyer.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.019 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.028 |
| 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".