Building transformative capacity in southern Africa: Surfacing knowledge and challenging structures through participatory Vulnerability and Risk Assessments
Bibliographic record
Abstract
Although participatory approaches are becoming more widespread, to date vulnerability assessments have largely been conducted by technocrats and have paid little attention to underlying causes of vulnerability, such as inequality and biased governance systems. Participatory assessments that recognise the social roots of vulnerability, however, are critical in helping individuals and institutions rethink their understanding of and responses to climate change impacts. This paper interrogates the contribution of Oxfam’s Vulnerability and Risk Assessment methodology to enabling transformation at both personal and institutional levels. Three Vulnerability and Risk Assessment exercises were conducted in Malawi, Botswana and Namibia by one or more of the authors in 2015 and 2016. Reflecting on these workshops, we explore the contribution that a process like the Vulnerability and Risk Assessment may bring to transformation. We conclude that these types of inclusive and representative participatory approaches can shift narratives and power dynamics, allow marginal voices to be heard, build cross–scalar relationships and enable the co-creation of solutions. Such approaches can play a key role in moving towards transformational thinking and action, especially in relation to climate change 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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".