Bibliographic record
Abstract
Climate change will dramatically affect labour markets, but labour law scholars have mostly ignored it. Environmental law scholars are concerned with climate change, but they lack expertise in the complexities of regulating the labour relationship. Neither legal field is equipped to deal adequately with the challenge of transitioning to a lower carbon economy and the effects of that transition on labour markets, employers, and workers. This essay considers whether a legal field organized around the concept of a ‘just transition’ to a lower carbon economy could bring together environmental law, labour law, and environment justice scholars in interesting and valuable ways. “Just transitions” is a concept originally developed by the North American labour movement, which has since been endorsed by important global institutions including the International Labour Organization, the UNFCCC, and the U.N. Environmental Program. Although ‘just transitions’ has received considerable policy attention, it has been under-explored by legal scholars. This paper marks an early contribution to this challenge. It explores the factual and normative boundaries of a legal field called Just Transitions Law and considers whether such a field would offer any new, valuable insights into the challenge of regulating a response to climate change.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".