Urgensi Pendekatan Multi dan Inter-disiplin Ilmu dalam Penanggulangan Bencana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intensity and serious impact of disasters threaten human life, including in Indonesia. A series of natural disasters such as floods, landslides, earthquakes, and tsunamis in the past decade have claimed thousands of lives and damaged property and destroyed social and cultural structures. Current pandemic as non-natural disaster also shows that Covid-19 become among deadliest of disasters. With the unpredictable characteristics of disaster events (especially natural and pandemic), it is urgent to find a collaboration model for effective disaster management. As a concept, an approach and a method disaster management is not a monodisciplinary, but cross-disciplinary, whether it is multidisciplinary, interdisciplinary or transdisciplinary. Using a description and information analysis approach using secondary data through the literature review, this study discusses the link and contribution issues of disaster management. The results of the discussion show that apart from being multidisciplinary, disaster management is also interdisciplinary and transdisciplinary. In the disaster management cycle, there are important roles that differ between multidisciplinary, interdisciplinary, and transdisciplinary. This preliminary finding may be useful for researchers, policy makers, disaster managers and others to start cooperating in reducing disaster risk. A more comprehensive and in-depth study is needed to see the relationship between disaster management and related sciences for strengthening disaster management in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 0.001 |
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 it