Spatial Interaction Between Regions: Study of the East Kalimantan Province, Indonesia
Bibliographic record
Abstract
City morphology can be formed due to interaction either spatially or the socio-economic community in it.This study aims to determine the classification of fast-growing and growing quadrant areas in East Kalimantan Province, the central growth region and the highest relationship between spatial interactions between the growth centers and the hinterland region.The type of this research is quantitative descriptive research because this research is presented with numbers during 2014-2018.Data used is based on secondary data obtained from the site of the Central Statistics Agency and other related agencies.The analytical tool used is the Klassen Typology and Gravity Index, then processed using Microsoft Excel.The results of the empirical study show that there is one area in East Kalimantan Province that is classified as Quadrant I (Fast Forward and Growing Area), namely East Kutai Regency, and there are dominant seven regions classified in Quadrant III (Rapid Developing Areas), and none occupy Quadrant I (Disadvantaged Region).The area is the center of growth in East Kalimantan Province, namely Berau Regency.Meanwhile, Samarinda City with the highest spatial interaction (attractiveness and potential) with a growth center in Kutai Kartanegara Regency (hierarchy I).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".