Bibliographic record
Abstract
This study intends to analyze a program using the SWOT Analysis method (strengths, weaknesses, opportunities and threats) in the disaster resilient Nagari program. The disaster resilient village program aims to provide an understanding to the community about disaster risk reduction and selfrescue efforts before the program is implemented. The type of research that I use is qualitative research with descriptive methods. Data collected by interview and documentation study, interview guides in the form of questions that have been prepared, the data collection tools that the authors use are cameras, cellphones, and recording devices. The selection of informants was carried out by means of purposive sampling. The instrument used was the researcher himself, for the validity of the data the author did so by means of source triagulation. From the results of the study it can be concluded that the strengths of this program are adequate human resources, the weakness of this program is the limited funds / budget that causes the program can not run optimally, the opportunities of this program the use of ecotourism as disaster risk reduction, threats from the program this is the low level of community knowledge in responding to the condition of the South Pesisir District which is prone to disasters.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".