Clouds or Sunshine in Katowice? Transparency in the Paris Agreement Rulebook
Bibliographic record
Abstract
This article identifies outstanding issues regarding the adoption of MPGs for the transparency framework of the Paris Agreement at the Conference of the Parties in Katowice in December 2018.The article first offers a definition of the concept of transparency, and reviews certain salient elements in the literature.This includes a warning that adopting transparency rules that elude questions of accountability of Parties for their domestic policies and equity in burden sharing may fail the objective of building the trust and confidence for which the transparency framework was adopted.The article next offers a brief overview of the requirements of Article 13 of the Paris Agreement, before assessing three key matters raised by the August 2018 draft of the MPGs in light of the literature and recent submissions by Parties.The article underscores the relevance of including a focus on ex ante accountability for climate policies in the way the transparency framework is set to operate, in view of the overall focus of the Paris Agreement on prevention of environmental harm.Comparing the potential of transparency rules to promote accountability of reporting and accountability of implementation of Parties' commitments, it further argues for the inclusion of both foci in the modalities.Lastly, the article highlights that the Paris Rulebook presents a unique opportunity to craft specific roles for non-Party stakeholders in the operation of the transparency framework, thereby developing the links between the United Nations Framework Convention on Climate Change and transnational climate governance initiatives.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".