Design options for the global stocktake : lessons from other review processes
Bibliographic record
Abstract
While the Paris Agreement (PA) has enshrined ambitious long-term objectives, the current actions of the Parties to the Agreement fall far short of these goals. The Global Stocktake (GST), established in Art. 14 of the PA, may help narrow this gap between ambition and action: its purpose is to review the implementation of the PA and to assess the collective progress of the international community towards Paris goals. While some general modalities on how to conduct the GST have been adopted, the details are still to be determined. The objective of this report is to analyze existing international regimes as regards their review processes, the contribution of these review processes to various governance functions and, finally, to derive lessons for the GST. Processes analyzed include: the design of the upcoming Global Stocktake itself, the Talanoa Dialogue (TD) which is the direct precursor of the GST, the Agenda 2030 High-Level Political Forum (HLPF), which features a regular stocktaking process focused on progress toward the Sustainable Development Goals (SDGs), the review processes of the UN human rights system (UNHRS) and the review processes and assessment panels of the Montreal Protocol (MP). The analysis of each review process is organised in four section: (1) political background and context, (2) technical and organisational details of the processes, (3) interface between the political and technical processes, and (4) how the review processes contribute to achieving the objectives of the respective regime, particularly governance functions of the regime (guidance and signal, transparency and accountability, and knowledge and learning).
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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 | 0.299 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.040 | 0.021 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".