Socio-Economic Adaptation After Natural Disasters In Langaleso Village
Bibliographic record
Abstract
On September 28, 2018, the earthquake and liquefaction disaster in Langaleso Village, Dolo District, Sigi Regency severely damaged several areas. In addition, other impacts of natural disasters that occur are resulting in changes in the social and economic conditions of the community. This is because the state of their agricultural land was wiped out due to the impact of liquefaction that occurred in Jono Oge village. The damage to the Gumbasa irrigation channel resulted in a decline in the community’s economy. The purpose of this study is to determine the form of adaptation of the Langaleso village community based on social and economic aspects both from before natural disasters, after natural disasters, and current conditions. The research method used is descriptive qualitative data analysis techniques, where data is obtained by observation techniques and interviews conducted in the field. And then, the information that has been received is then processed to get the results of the research objectives. The results of this study are that the community is still trying to adapt to existing needs at present conditions. The main problem that the village community experiences are in the economic sector. The majority of the main work of the community is farmers. Still, with the damage to the irrigation system, the district has not been able to carry out agricultural or plantation activities
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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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".