Place‐Based Sustainability Transformations for Just Futures: A Systematic Review
Bibliographic record
Abstract
ABSTRACT The planet is facing enduring and intersecting challenges from climate change, land degradation, habitat and biodiversity loss, as well as social inequalities. To achieve sustainability in the face of these crises, transformative changes are essential. While the path towards a more sustainable future has the capacity to bring net social benefits, it also holds the potential to exacerbate vulnerabilities. As such, there is growing recognition that social equity and justice must be central to action for sustainability transformations. However, it remains unclear what characterises a ‘just transformation’ and how to achieve it. To develop a baseline understanding of how social justice is integrated into sustainability transformations and to help guide research and practice in this emerging field, we present a systematic literature review of 125 papers that explicitly account for social equity and justice in research on place‐based transformations. Results reveal considerable variation and ambiguity in how the concepts of transformation and equity are employed, and highlight a focus on a narrow set of systems, with a large number of papers focusing on energy and urban transformations, located primarily in the Global North. While distributional and procedural dimensions of justice are frequently addressed, contextual and restorative justice remain underexplored. We identify key areas that require future attention in research and practice, including promoting interdisciplinary research that champions global inclusivity as well as a more explicit consideration of contextual justice and place.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".