Measuring accessibility and island development in Ambon City
Bibliographic record
Abstract
Island studies has thus far mostly focused on the limitations, isolation and marginality of island communities. However, recent research into island cities, or urban island studies, provides an analytic lens or research perspective that can be used to understand an island’s diversity and to encourage researchers to identify island characteristics that have an impact on the function of cities and population centers on islands. One of the factors that inhibit the development of island cities is the limited availability of land area and resources, causing island city regions to depend on other regions to fulfill the population’s needs and provide basic services to the population, which puts islands in a vulnerable position because of transportation accessibility problems. This study was conducted using the Transit Opportunity Index (TOI) method to observe the relationship between transportation accessibility and economic growth in island city regions. The result of the analysis showed that transportation accessibility indirectly affects economic growth in every district/city in Maluku Province. Sea transportation accessibility better illustrates the condition of transportation accessibility of Ambon City and other districts/cities in Maluku Province compared to sea and air transportation accessibility.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".