Assessing Human Resources Development in Volcano Observatories Using the Knowledge, Attitude, and Practice Survey
Bibliographic record
Abstract
The purpose of this study was to assess the role played by the International Training Course, given by the Center for the Study of Active Volcanoes (CSAV) at the University of Hawai’i at Hilo, in the development of human resources for volcano observatory staff around the world. The study design included a literature review, interviews with representatives from 10 national volcano observatories, and electronic surveys designed and conducted by Florida International University, targeting graduates of training courses sponsored by the Volcano Disaster Assistance Program (VDAP), a cooperative partnership between the US Agency for International Development (USAID) and the US Geological Survey (USGS). The knowledge, attitude, and practice (KAP) method was used to develop a survey and a composite KAP index. Of the 92 individuals who answered the survey, 47 had completed the CSAV course. Two groups were formed from the survey respondents: (1) 47 people who were CSAV graduates; and (2) 45 people who did not take CSAV training. An independent samples t test and a one-way multivariate analysis were conducted to compare the KAP index and the three subindices. The heads of the volcano observatories identified the CSAV International Training as one of the most useful educational offerings relevant to volcano observatory operations available worldwide. The statistical analysis showed significant differences in the KAP index, knowledge subindex, and practice subindex between the two study groups. Results indicate that CSAV graduates are in a better position to attain self-sufficiency in studying and monitoring volcanoes, and by doing so, contribute more effectively to mitigation efforts for volcanic unrest and eruptions.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".