When two movements collide: Learning from labour and environmental struggles for future Just Transitions
Bibliographic record
Abstract
The term ‘Just Transition’ (JT) emerged from the 1970s North American labour movement to become a campaign for a planned energy transition that includes justice and fairness for workers. There is diversity in the JT narratives and ambitions that different actors put forward regarding its aims and strategies. This article critically reviews academic and grey literature on the JT in the Global North and South Africa to examine how labour, advocacy, private sector, and governmental actors frame and formulate the JT, and how narrative patterns across actors can signal transformative justice. Highlighting the JT’s origins, we fill a gap in transition literature by reintroducing the labour perspective into an analysis of affirmative and transformative justice, and propose an original theoretical framework that unites scholarship in environmental and labour studies. JT proposals are examined through an analysis of the actors, approaches, and tensions across five key themes: depth & urgency, scale & scope, identity & inclusion, material equity, and participation & power. Finally, we synthesise trends in our findings in relation to prominent JT discourses in the literature – Green Growth, Green Keynesianism, Energy Democracy, and Green Revolution – and discuss the transformative potential of JT alliances and coalitions going into the future.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.052 |
| Scholarly communication | 0.024 | 0.042 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".