Adaptation interventions and their effect on vulnerability in developing countries: Help, hindrance or irrelevance?
Bibliographic record
Abstract
This paper critically reviews the outcomes of internationally-funded interventions aimed at climate change adaptation and vulnerability reduction. It highlights how some interventions inadvertently reinforce, redistribute or create new sources of vulnerability. Four mechanisms drive these maladaptive outcomes: (i) shallow understanding of the vulnerability context; (ii) inequitable stakeholder participation in both design and implementation; (iii) a retrofitting of adaptation into existing development agendas; and (iv) a lack of critical engagement with how ‘adaptation success’ is defined. Emerging literature shows potential avenues for overcoming the current failure of adaptation interventions to reduce vulnerability: first, shifting the terms of engagement between adaptation practitioners and the local populations participating in adaptation interventions; and second, expanding the understanding of ‘local’ vulnerability to encompass global contexts and drivers of vulnerability. An important lesson from past adaptation interventions is that within current adaptation cum development paradigms, inequitable terms of engagement with ‘vulnerable’ populations are reproduced and the multi-scalar processes driving vulnerability remain largely ignored. In particular, instead of designing projects to change the practices of marginalised populations, learning processes within organisations and with marginalised populations must be placed at the centre of adaptation objectives. We pose the question of whether scholarship and practice need to take a post-adaptation turn akin to post-development, by seeking a pluralism of ideas about adaptation while critically interrogating how these ideas form part of the politics of adaptation and potentially the processes (re)producing vulnerability. We caution that unless the politics of framing and of scale are explicitly tackled, transformational interventions risk having even more adverse effects on marginalised populations than current adaptation.
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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 | 0.051 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".