Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm
Bibliographic record
Abstract
The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".