Reflections on the first Global Stocktake of the Paris Agreement
Bibliographic record
Abstract
This commentary reflects on the first Global Stocktake (GST) under the Paris Agreement on climate change to offer insights for advancing climate actions and informing future GST cycles. The first GST, which concluded at COP28 in 2023, demonstrates the vital importance of a comprehensive, balanced, and inclusive approach to multilateral climate action. The GST's call to transition away from fossil fuels is an important political achievement. Yet, the GST outcome also reveals gaps, shortcomings, and potential dangers ahead. Future climate negotiations, we argue, would benefit from a more integrated, holistic perspective, and more nuanced balancing of ambition and implementation. More needs to be done to protect human rights, increase loss and damage funding, go beyond technological solutions, and address gender-differentiated consequences of climate change. Moreover, a great deal of work, including by nonstate actors, will be required to ensure the first GST translates into real action on the ground.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 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".