Bibliographic record
Abstract
The rapid growth of passenger and freight traffic in the Asia-Pacific region has come in hand with greater environmental concerns. Affirming its growing influence, the Asia-Pacific has widely contributed to recent ICAO actions to develop multilateral bases for market-based environmental measures in international aviation as an avenue to mitigate greenhouse gas (GHG) emissions. States in the Asia-Pacific regions also have engaged in some laws and policies aimed at contributing to climate change mitigation, in particular by reducing GHG emissions. Yet, there is no broad consistency in the measures undertaken, or a particular focus on international aviation. Individual states in the Asia-Pacific region have approached the objective of mitigating aviation impacts very differently, from countries that have only adopted embryonic climate laws to countries with fully operational carbon-pricing schemes. Given the spirit of the Chicago Convention 1944, it is principally important to make every effort to come up with a global approach through the ICAO. However, alternative approaches should also be considered. The alternatives may well stem from the “bottom-up” collective linkages being created between regional or single-state ETSs, such as the bilateral trade link that will be forged between the EU and Australia. This model of cooperation could be an alternative to stalling multilateral negotiations on a global regime.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.015 |
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".