The Shift Dynamics in Digitalization Distribution Pattern of Informal Sector Startup Users in Palembang City, Indonesia
Bibliographic record
Abstract
Digitalization in the informal sector, specifically through the use of startups, is an important phenomenon for improving sales and revenue.Palembang City has specifically experienced an increase in the development of digitalization in this sector.Therefore, this study aimed to investigate the dynamics of shifting digitalization patterns among informal sector startup users in Palembang City.A quantitative method was used with a survey as the main approach, while data were obtained through questionnaires and GPS.The sample comprised 384 respondents selected through proportional random area sampling in 18 districts of Palembang.Subsequently, data were analyzed using cross-tabulation and spatial analysis with Average Nearest Neighbor (ANN).This is a spatial analysis technique for measuring spatial proximity or geographic distribution patterns of a collection of points in an area and Getis Ord General Gi.The results showed an increase in income among users, with digitalization patterns that consistently increased over time.Moreover, startup users in the informal sector of Palembang City mostly dominated the sale of goods.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".