Local Participatory Flood Hazard Mapping’s Assessment and Coping: A Conceptual Model of Sustainability in Downstream Area of Belu Regency in the Western Timor Island, East Nusa Tenggara Province, Indonesia
Bibliographic record
Abstract
This study applies interview, Focus Group Discussion (FGDs), Participatory Geographical Information Systems (pGIS), and a conceptual model of sustainability (CMS) using risk perception of local community to map flood hazard and assess the social and cultural copings to cope with river flooding in downstream areas i.e., Lasaen, Umatoos, and Fafoe villages of West Malaka Subdistrict of Belu Regency, Indonesia. The results of this study indicate that the rural-river flooding was inundated at all three villages. The cycle of flood is twenty-years per event (1939, 1959, 1975, 1999 through 2000), and from 2000 its occurrence was each year until 2012. Based on interviews and FGDs, the information of flood characteristics of Lasaen and Fafoe villages were similar, but Umatoos village was not. The single longevity of flood inundation was in Fafoe village (1 week-1 month). Whilst Lasaen and Umatoos villages were experiencing less duration of flood inundations (0-7 days to 14-21 days). Lasaen and Umatoos Villages were dealing with flood depth’s variation from the lowest depth (0-50cm) to its deepest (251-300cm). For CMS, the most invaluable coping that might be sustainable was cultural capital. Both social and cultural coping enhancements were implemented by local community. The minimum and lack of both these transformable sub-copings were still the problem in the discourse unit of sustainability. As each sub-coping would be overlapped if there has no sufficient distribution of it, utilized by the local community. The genuineness local knowledge of community in applying their social and cultural copings in sustainability is seen as a unique reference and a useful form of local wisdom which can be highlighted and adopted as an effective and/or example discourse analysis by the other rural villages in developing nations that are also still struggling and coping with flood disaster.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".