Analisis Potensi Sektor Unggulan dan Pemetaan Kemiskinan Masyarakat di Wilayah Maminasata Sulawesi Selatan
Bibliographic record
Abstract
The purpose of this study is to map sectoral superior potential and changes in regional poverty levels in the Mamminasata region. The method used in this study is qualitative descriptive, using quantitative analysis tools, leading sector analysis tools such as Location Quotient (LQ), Growth Ratio Model (MRP), Overlay Analysis, and Klassen Typology. The results of the study show that there is still a high level of disparity in leading sectors in the Mamminasata region. The results of the analysis show that Makassar City has 12 leading sectors, Kab. Gowa, 7 leading sectors, Maros District 4 leading sector, and Takalar District 3 superior sector. While the results of the Klassen Typology analysis show that only Makassar City consistently shows 12 superior sectors in quadrant I (advanced and fast-growing sectors). While other regencies are only 3 sectors which are in quadrant I, other economic sectors are growing but depressed, there are also potential ones. In fact, Maros Regency and District. Takalar has 11 sectors that are still lagging behind. Based on the poverty mapping of districts / cities in the Mamminasata area, it shows that Makassar City and District. Gowa has an average number of poor people lower than South Sulawesi Province. Takalar Regency tends to be the same as South Sulawesi province, and there are paradoxical symptoms between GDP and poverty. Whereas Kab. Maros is above the poverty average of Prov. South Sulawesi. In aggregate poverty in the Mamminasata area declined during the study period. Makassar City, Kab. Gowa, Kab. Maros, even though the rate of growth declined, the number of poor people also declined. Whereas Takalar Regency has increased GDP but its poverty has also increased.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".