Climate Resilience and Social Assistance in Fragile and Conflict-Affected Settings
Bibliographic record
Abstract
This paper aims to improve our understanding of the nature, causes, and multiple dimensions of how social assistance may address climate vulnerability and resilience within fragile and conflict-affected settings (FCAS), as part of the inception phase of the Better Assistance in Crises (BASIC) Research programme. Over recent years, social assistance, such as cash transfers and voucher programmes, has been seen as a way of reducing the impacts of climate-related shocks and stressors, and of increasing the resilience of recipient households and communities. It has also been seen as a mechanism for delivering adaptation funding, showing promise in tackling short-term shocks as well as longer-term adaptation to climate change. Yet despite FCAS hosting some of the most vulnerable populations in the world, so far there has been little attention to these settings. We examine the linkages between social assistance and climate resilience in FCAS and in turn, implications for BASIC Research. Specifically, we ask what the evidence is on whether existing approaches to social assistance are appropriate to reducing climate vulnerabilities and building climate resilience in FCAS, and, if not, how they might be reformed. We address this through three sub-questions. First, what are the major conceptual discussions on climate resilience and social assistance, and what is the extent of work in FCAS? This is addressed in section 2.1, based on an extensive literature review. Second, to what extent does the literature on social assistance and climate resilience apply to the particular concerns of FCAS? This is covered in section 2.2, based on a framework informed by work in political economy and political ecology. Third, what are possible future research directions? We conclude with reflections on what BASIC Research may contribute in section 3.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".