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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".