The Asia-Pacific Partnership on Clean Development and Climate: What it is and What it is Not
Bibliographic record
Abstract
There has been a growing interest in the Asia-Pacific Partnership on Clean Development and Climate, a multilateral initiative of six founding partner countries in the region - Australia, China, India, Japan, Republic of Korea and the US - for addressing air pollution, energy security and climate protection in a way that facilitates development and poverty eradication. Canada became the 7th member in October 2007. This paper presents an overview of the Asia Pacific Partnership, beginning with a literature review. This is followed by a comparison of the initiative with the EU energy and climate change package and an examination of the role that the Partnership can play in the emerging landscape of reorganising climate cooperation in both its formal and informal aspects: bilateral and multilateral technology partnerships which are compatible with the UNFCCC objective; and development of tools for measuring progress such as benchmarking, performance indicators and energy efficiency or environment indices. Through coordination with actions in each aspect in other parts of the world or those on a global scale, the Partnership could have wider implications beyond its partner countries.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".