Understanding place-based “just transitions” in Ireland: a co-creation approach
Bibliographic record
Abstract
The term “just transition” has proliferated in recent years but remains a deeply contested term. While the focus has increasingly shifted to ensuring just processes as well as outcomes as we transition to a low-carbon economy, there has also been growing recognition of the necessity of place-based approaches. This paper shares a mixed method, co-creation case study of three Irish communities, drawing on the Capability Approach (CA) in order to operationalise a place-based approach to/for a just transition. We focus on identifying those factors that convert resources into capabilities and enhance well-being. Examining these conversion factors (personal, environmental, social and economic) paves the way for understanding barriers to and pathways towards a just transition in particular places. Our research identifies the need to interrogate the relevance of the “just transition” to particular communities and to recognise “varieties of transitions” matched to local conditions. We argue that Just Transition must be seen within the wider history of regional, social and spatial inequalities and thus an agonistic approach that uncovers past injustice is critical to imagining a new future. Finally, we argue that significant governance innovation is required that is both bottom-up and coupled with targeted resourcing of existing and new institutions. Without a focus on spatial and social justice, our responses to the climate and biodiversity crises will beget new crises of wellbeing and quality of life. With a focus on spatial and social justice we can give momentum to climate action and simultaneously address historical and existing socio-spatial inequalities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".