Beyond redistribution: a framework for reparative just transitions
Bibliographic record
Abstract
Abstract Prevailing just transition proposals often assume the continuity of colonial land relations and see ‘greener’ industrial development as a pathway to prosperity for all. Our paper aligns with and aims to contribute to emerging scholarship that questions these assumptions. Through a review of interdisciplinary just transition research, we map a distinction between mainstream, critical and reparative approaches to just transitions. Mainstream perspectives, largely rooted in the global north, focus on labour justice, while critical just transition scholarship addresses intersectional injustices and amplifies grassroots perspectives, particularly from the global south. Reparative approaches extend this critique by reckoning with the interconnected historical legacies of colonialism and industrial development, though empirical examples of this approach remain sparse. To differentiate these approaches, we introduce a 5R framework for just transitions—recognition, representation, redistribution, resurgence, and (settler) responsibility—foregrounding Indigenous resurgence as a crucial yet often overlooked element of repair. To illustrate this framework, we draw on a community-directed collaboration with the Tremembé da Barra do Mundaú Indigenous community in Ceará, Brazil. We conclude by inviting environmental and social scientists to consider more historically grounded and relationally reparative just transition approaches. Such approaches are essential, though not sufficient, for realizing the more transformative aims of just transitions.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.057 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".