Toward Climate Justice: Resilience and Adaptation Strategies for Vulnerable Developing Countries
Bibliographic record
Abstract
Toward Climate Justice: Resilience and Adaptation Strategies for Vulnerable Developing Countries Surender Kumar¹, Maureen O'Sullivan², Abhnash K Bains³, Vidya Padmakumar⁴, Silverio Allocca⁵, Gelito Inacio Franco Sululu⁶, Cristiano Carocci⁷, Anu Gauba⁸, Clare Walker⁹, & Progga Parmita Chowdhury¹⁰ ¹ ALA Fellow, Melbourne University, Australia / India, ² Fellow, Oxford Centre for Animal Ethics, University of Galway, Ireland, ³ Ishkama Global Change CIC, London, United Kingdom, ⁴ EcoDiversity Lab, New Hazelton, British Columbia, Canada, ⁵ Researcher & Analyst, Italy, ⁶ National Representative, Commonwealth Youth Climate Change, Mozambique, Africa, ⁷ Political and Cultural Analyst, Italy, ⁸ Principal & Professor, Department of Nursing, GD Goenka University, Gurgaon, Haryana, India, ⁹ Clare Walker Consulting Ltd, England, ¹⁰ Independent Researcher, National University of Bangladesh Abstract Climate change disproportionately affects developing countries due to their limited adaptive capacity, socio-economic constraints, and increased exposure to climate-related hazards. This paper investigates resilience and adaptation strategies through the lens of climate justice, highlighting the importance of equitable and inclusive approaches. Using a secondary research methodology, it synthesizes theoretical frameworks and empirical data from peer-reviewed literature, international reports, and climate finance databases. A quantitative analysis evaluates adaptation investments and their impact on reducing vulnerability, supported by numerical calculations and country-specific case studies. The study concludes with actionable policy recommendations aimed at achieving both sustainability and justice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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; both teacher heads agree on what is shown here.
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".