Bibliographic record
Abstract
The International Studies (IS) community has not always paid attention or known what to do with climate change (Javeline 2014; Keohane 2015; Green and Held 2017; for a critical take on this observation, see Cashore and Bernstein 2023). The former is now untenable, and the latter had better change quickly. Climate change entails challenges for the whole IS community, whether one cares to study climate change directly or not. In this brief essay, I discuss why things are different today and explore some of what the difference means for theorizing and practice in the IS community. Climate change is here. It is not a problem for our grandchildren. It is here now. Those paying attention have worried about its imminent arrival for a while, but the climatic events in the last few years have made the realization of climate change’s contemporality difficult to avoid for the whole world. There is a parallel to be found in the relationship of climate change to the IS community. A significant but relatively small group of scholars have worked on climate politics for decades, with the literature emerging in the 1990s (for some of the earliest work, see, e.g., Grubb 1993; Young 1994; Rowlands 1995; Paterson 1996; Gupta 1997; Betsill and Pielke 1998) and growing significantly since. Now climate change is unavoidable for the whole IS community as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".