Climate Leadership Through Storylines: A Comparison of Developed and Emerging Countries in the Post-Paris Era
Bibliographic record
Abstract
The expectation of developed countries’ leadership is institutionalised in the United Nations’ climate agreements. Hence, climate leadership discussion often builds on the experience of the Global North and ignores the non-western contexts. This article analyses how climate leadership is socially constructed through discourse by developed and emerging countries. Here, developed countries were limited to Australia, Canada, the EU, Japan, New Zealand, and the US, and emerging countries to the BASIC group, comprising Brazil, China, India, and South Africa. The analysis was conducted by drafting storylines and discourse-coalitions based on national speeches at the UN climate conferences in 2016–2019. The results underline that the two sides differ primarily in perceptions of leadership responsibility and problematisation but share ideas about transition as a problem solution. Furthermore, neither side constructs their own leadership on the basis of responsibility, and the demand for collective responsibility particularly benefits the Global North.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".