Public-private Partnership for Operating Safety Experience and Education Center - A Comparative Analysis of the US, Canada, and Japan -
Bibliographic record
Abstract
이 연구는 민관협력을 통한 지방정부의 재난안전체험관 효율적 운영 방안을 제시하는데 목적이 있다. 먼저, 이론적 논의를 통해 재난안전교육 및 민관협력의 개념과 재난안전체험관의 기능을 알아보 았다. 효율적 재난관리체계에 영향을 미치는 레질리언스는 재난안전교육을 통해 강화될 수 있으며, 이를 위해 재난안전체험시설이 필요함을 알 수 있다. 그리고 우리나라 재난안전체험관의 운영 예산및 프로그램 현황을 분석하였고, 외국의 재난안전체험관 운영 사례를 분석하여 벤치마킹에 필요한 시사점을 도출하였다. 미국과 일본의 재난안전체험관은 운영방식과 체험프로그램에 서로 차이가 있으며, 각기 다른 의미의 시사점을 가지고 있다. 먼저 미국의 사례는 민간부문이 재난안전체험관 운영에 적극적으로 참여하고 있다. 다음으로 일본의 재난안전체험관은 각 지역의 자연 재해 상황및 특성에 맞춘 안전체험관 프로그램을 운용하고 있다는 것이다. 이 같은 해외 사례의 시사점을 적용하기 위한 선결조건으로 이 연구는 지역사회 구성원들의 재난안전교육에 대한 관심 제고와 체험관 건립 초기단계 참여, 초⋅중등 학생에 대한 안전교육의 의무화 등을 제시하였다.This study aims to suggest the optimal operational plan of safety experience and education center (i.e. safety village) through public-private partnership. Disaster safety education is expected to enhance disaster resilience and the educational impacts could be augmented when hands-on experiences are emphasized during the education process. However, it appears difficult for local governments with limited resources to obtain sustainable budgets to maintain and operate such education facilities. Thus, we reviewed and compared the operational budget and safety experience programs of various safety villages in the US, Canada and Japan and drew out useful benchmarking implications for local governments in Korea. We found that the private sector has been actively engaged in operating safety villages in the US and Canada, while the safety experience centers in Japan offer experience programs tailored to the unique disaster characteristics of each local government. For the improved operation of safety experience and education center in Korea, we suggest compulsory safety education to ensure citizen’s participation and operational sustainability of the center.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".