Principles for the consideration of intersectionality in place‐based disaster risk governance in islands
Bibliographic record
Abstract
Abstract This paper advances disaster risk governance (DRG) research and practice by incorporating elements of intersectionality and place‐based thinking. Intersectionality provides a crucial yet underutilised lens to examine power, positionality, and individuals' experiences facing disasters and other climatic events. Through six principles and using examples from small islands and a synthesis of the literature, this paper presents an intersectional approach for DRG to support inclusive and contextualised actions: (i) individuals are multi‐dimensional and complex; (ii) identities and vulnerability are not predefined; (iii) spatial and temporal differences influence the expression of identities; (iv) materiality of ecological systems influences intersectionality; (v) power relations are central the emergence of social processes and epistemologies; and (vi) positionality plays an important role in defining risk reduction agendas and choices. This paper examines how an intersectional perspective generates pathways to address the root causes of vulnerabilities to disasters beyond the ‘one size fits all’ approaches promoted globally.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".