ASEAN SINGLE AVIATION MARKET AND INDONESIA - WILL IT SURVIVE AGAINST THE GIANTS?
Bibliographic record
Abstract
To say that Indonesia is an enigma in air transport is an understatement. On the one hand, the demand for air transport in Indonesia is higher in proportion to its GDP per capita. Its economy can be expected to grow 6% to 10% annually. A single aviation market could add another 6% to 10% growth in sheer demand. It is one of the wealthiest countries in the world, being the 16th richest country currently, and, according to an Airbus forecast, will be the 7th richest in 2030. Yet its airports are badly in need of expansion, its infrastructure is bursting at its seems, and above all, its airlines are strongly resisting liberalization of air transport in the region for fear of being wiped out by stronger contenders in the region. Against this backdrop, it is incontrovertible that Indonesia's civil aviation is intrinsically linked to regional and global considerations. Indonesia's archipelagic topography makes its people heavily reliant on safe, regular and reliable air services that may connect them not only internally but also to the outside world. A single aviation market in the ASEAN region will bring both benefits to Indonesia and challengers to its air transport sector. This article discusses the economic and regulatory challenges that Indonesia faces with the coming into effect of the ASEAN Single Aviation market in 2015.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".