Urgensi Pendekatan Multi dan Inter-disiplin Ilmu dalam Penanggulangan Bencana
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".