Occupational Correlation to the Level of Community Welfare Using The Apriori Algorithm (Case Study: Mangga Village)
Bibliographic record
Abstract
Mango village is one of the areas that participates in various welfare programs for the local community, where in this village there are still people who are far from prosperous because of various factors that affect the welfare of the local community, one of which is the jobs owned by the community. Therefore, it is important for people to understand that work also greatly influences the level of welfare for their own lives, so that they are fulfilled in the economy, education and others. Therefore the author wants to create a system that can assist the government in developing community welfare programs in Mango Village by knowing the relationship between work and the level of social welfare. After carrying out the above case trials with minimum support = 25%, confidence = 100% so that the rule results that meet the support and confidence values are obtained, it can be concluded that if the assets owned are A4 (motorcycles), with T2 dependents (3-4), with M2 jobs (Private Employees) and A4 assets (Motorcycles), with P2 income (> 1,000,000 - < 2,000,000), then enter K2 (welfare stage II) with a support value of 20%, 100% confidence.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".