Comparative Study of Reporting for Transparency under International Agreements on Climate Change and Ozone Protection: The Case of the Philippines
Bibliographic record
Abstract
Transparency is crucial for the effective implementation of the United Nations Framework Convention on Climate Change (UNFCCC) and the Montreal Protocol on Substances that Deplete the Ozone Layer. This paper examines the status of reporting for transparency by developing countries under these international regimes. In doing so, it selects the Philippines as a case, since the country is apparently late in the submissions of national reports under the UNFCCC. This study aims to identify possible factors hindering the Philippines from reporting as required under the Convention. To this end, it presents the results of a comparative study with the reporting by the Philippines under the Montreal Protocol by utilizing the framework of "enforceability analysis" as well as the concepts of "scale" and "scaling". The comparison in terms of five factors for enforceability finds that the data collection for reporting under the Montreal Protocol is relatively easier to implement in the Philippines. Furthermore, upscaling of the issue of climate change on the administrative scale is found to impose additional burden on capacity development in the country. The findings suggest an increasing need for donors to find a good balance between assisting developing countries deliver reports for transparency and facilitating their longer-term capacity development.
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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 | 0.050 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".