Bibliographic record
Abstract
To understand how regulators are developing systematic frameworks to make climate pledges investible, robust, and actionable, MIT Science Policy Review spoke with Catherine McKenna, founder and principal of Climate and Nature Solutions, former chair of the United Nations High-Level Expert Group on Net-Zero Commitments for Non-State Entities, Canada’s Minister of Environment and Climate Change and Minister of Infrastructure, and negotiator of the Paris Agreement at COP21. Ms. McKenna has led discussions on global carbon markets, directed Canada’s landmark national climate plan, and oversaw the development of Canada’s first National Infrastructure Assessment to encourage net-zero emissions by 2050 in Canada. Through our conversation, we discuss the global economic and regulatory shifts required to create ethical and actionable pledges–specifically the need to regulate, set net zero targets, limit use of voluntary credits, align advocacy with action, include people and nature in a just transition, and increase accountability.
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 | 0.153 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.037 | 0.046 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".