Qualitative Loop Analysis of Social-Ecological Connectivity: The Case of Bima Bay, West Nusa Tenggara
Bibliographic record
Abstract
The coastal area of Bima Bay will continuously experience increased development for the next few years along with its city development as “Waterfront City” and as a tourism village by the decision letter of the West Nusa Tenggara governor. The data used in this research are primary and secondary data with a purposive sampling method. The analysis results show that: 1) the basic network model does not significantly differ from the simulation model, 2) loop analysis based on seven scenario simulations combines six nodes with the assumption that if node Up, Ad, Hp, P, and Jv is unavailable, so the nodes gaining negative effect are Tt, Ti, Sp, II, Ic, and Dw. Sustainable management effort of the ecosystem in Bima Bay by observing the network connection between SES components to find out the component giving positive and negative effects in management policy-making. The simulation model using the goodness of fit test for model statistic obtains p-value 0.96 which means H0 received since p-value 0.96 > 0.05 points. There need sustainable efforts to maintain the Bima Bay ecosystem by observing the impact of network relation across the components in SES to find out the component with positive and negative impact in making management policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".