BUILDING A GREEN ECONOMY: ADVANCING CLIMATE JUSTICE THROUGH ENVIRONMENTAL-LABOR ALLIANCES*
Bibliographic record
Abstract
This article explores the role of environmental-labor coalitions in creating opportunities to promote green jobs and to shape climate change policies. The development of a green economy is critical for combating climate change, as well as for addressing rising unemployment and the expansion of precarious work. My research is based on a qualitative study of environmental-labor coalitions in California, United States, and British Columbia, Canada, including fifty-six in-depth digitally recorded interviews with environmental and labor movement leaders and policymakers. The findings point to the importance of three key mechanisms that shape the success of these coalitions: (1) drawing on the strength of organizational diversity, (2) fostering relationships of trust that allow organizations to adopt flexible ideologies, make concessions and tradeoffs, and create hybrid identities, and (3) frame bridging by local social justice organizations to mitigate conflict between environmental and labor movements.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| 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".