Bibliographic record
Abstract
Abstract International Relations (IR) generally treats environmental issues like climate change as case studies to illustrate political phenomena such as conflict, cooperation in global governance, and migration. Yet, given how biodiversity, ecosystems, and habitability shape international political and social relations, environmental issues deserve to be core topics in their own right. In like manner, IR scholars and IR policy elites tend to relegate Global South perspectives, concerns, and ideas to the margins of the discipline and global governance institutions (GGIs). Countries in the Global North—which have disproportionately contributed to the climate crisis—paradoxically lead GGIs and are thus centred in scholarship, including within norms research in IR (NRIR). This chapter traces the construction of global environmental governance norms, for whom these norms have been constructed, and the consequences of scholars and practitioners maintaining a narrow focus on governance centred on the present day. In examining ‘whose norms matter?’ in the global environmental governance space, this chapter argues that scholars can—and must—engage in new patterns of socialization within the field, moving climate change to the fore and bringing perspectives of Global South and Indigenous peoples from the margins to the centre. In so doing, scholars can challenge historical constructions of norms within IR and develop deeper understandings of norms that are better adapted to explain past, present, and future global challenges.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".