Participatory Action Research for Dealing with Disasters on Islands
Bibliographic record
Abstract
Much disaster research has a basis in non-island case studies, although monodisciplinary disaster-related research across past decades has often used case studies of individual islands. Both sets of work contribute to contemporary ‘participatory action research’ which investigates ways of dealing with disasters on islands. This paper asks what might be gained through combining disaster research, island studies, and participatory action research. What value does island studies bring to participatory action research for dealing with disasters? Through a critical (not comprehensive) overview of participatory action research for dealing with disasters on islands, three main lessons emerge. First, the island context matters to a certain degree for disaster-related research and action. Second, islandness has much more to offer disaster-related research than is currently appreciated. Third, more studies are needed linking theory to evidence found on the ground on islanders’ terms. Limitations of the analyses here and future research directions are provided.
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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 | 0.090 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".