Special issue: the poverty-inequality-environment frontier in the age of the crises
Bibliographic record
Abstract
This Special Issue is part of a joint initiative on “Financial Crises, Poverty, and Environmental Sustainability” by the Sussex Sustainability Research Project (SSRP) of the University of Sussex, the UNDP-UNEP Poverty-Environment Action for Sustainable Development Goals, and the United Nations Research Institute for Social Development (UNRISD). The aim of the project is to foster an evidence-based understanding of the multiple and complex ways in which poverty and environmental dynamics interact in moments of economic crises and how this interaction can be managed in a way that facilitates the transition to sustainability. The Special Issue includes the following papers: 1. The Great Stagnation and environmental sustainability: A multidimensional perspective Bernardo Cantone, Alexander S. Antonarakis, Andreas Antoniades (University of Sussex) 2. Confronting inequality in the ‘New Normal’: hyper-capitalism, proto-socialism and post-pandemic recovery Tim Jackson (University of Surrey), Peter A. Victor (York University, Toronto) 3. Transformative social policies as an essential buffer during socio-economic crises Isabell Kempf, Paramita Dutta (UNRISD) 4. Alleviating debt distress and advancing the Sustainable Development Goals Howard Haughton (King’s College London), Jodie Keane (Overseas Development Institute - ODI) 5. Bracing for the typhoon: Climate change and sovereign risk in Southeast Asia John Beirne, Nuobu Renzhi (Asian Development Bank Institute, Tokyo), Ulrich Volz (SOAS) 6. Climate shocks and poverty persistence: Investigating consequences and coping strategies in Niger, Tanzania, and Uganda Vidya Diwakar, Antoine Lacroix (Overseas Development Institute – ODI) 7. Class and climate change adaptation in rural India: Beyond community-based adaptation models Maryam Aslany (University of Oxford), Shannon Brincat (The University of the Sunshine Coast)
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.087 | 0.028 |
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".