Accredited qualifications for capacity development in disaster \nrisk reduction and climate change adaptation
Bibliographic record
Abstract
Increasingly practitioners and policy makers working \nacross the globe are recognising the importance of \nbringing together disaster risk reduction and climate \nchange adaptation. From studies across 15 Pacific island \nnations, a key barrier to improving national resilience \nto disaster risks and climate change impacts has been \nidentified as a lack of capacity and expertise resulting \nfrom the absence of sustainable accredited and quality \nassured formal training programmes in the disaster risk \nreduction and climate change adaptation sectors. In the \n2016 UNISDR Science and Technology Conference \non the Implementation of the Sendai Framework for \nDisaster Risk Reduction 2015–2030, it was raised that \nmost of the training material available are not reviewed \neither through a peer-to-peer mechanism or by the \nscientific community and are, thus, not following quality \nassurance standards. In response to these identified \nbarriers, this paper focuses on a call for accredited formal \nqualifications for capacity development identified in the \n2015 United Nations landmark agreements in DRR and \nCCA and uses the Pacific Islands Region of where this \nis now being implemented with the launch of the Pacific \nRegional Federation of Resilience Professionals, for \nDRR and CCA. A key issue is providing an accreditation \nand quality assurance mechanism that is shared across \nboundaries. This paper argues that by using the United \nNations landmark agreements of 2015, support for a \nregionally accredited capacity development that ensures \nall countries can produce, access and effectively use \nscientific information for disaster risk reduction and \nclimate change adaptation. The newly launched Pacific \nRegional Federation of Resilience Professionals who \nwork in disaster risk reduction and climate change \nadaptation may offer a model that can be used more \nwidely.
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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 | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.123 | 0.031 |
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".