On the horizon: The futures of IR
Bibliographic record
Abstract
This Special Issue celebrates the 50th anniversary of Review of International Studies . Since 1975, the Review has published over 200 issues and over 1300 articles. The journal has played a key role in shaping the discipline of International Relations (IR), leading, or critically intervening in, key debates. To celebrate 50 years of Review of International Studies , we have curated a Special Issue examining the challenges facing global politics for the next 50 years. IR has regularly turned its attention backwards towards its historical origins. Instead, we look to the future. In this Introduction, we start by outlining four traditions of future-oriented thinking: positivist, realist prediction; planning, forecasting, and scenario-building; utopian dreams of an ideal political future; and prefigurative thinking in activist politics. From these traditions, we learn that thinking about the future is always thinking about the present. We then outline four themes in the Special Issue articles: How do we think about the future at all? How do we think about imperial pasts and the ongoing questions of colonization and racialization in the present? How will technological change mediate and generates geopolitical change? How are socioecological crises, and in particular climate change, increasingly shaping how we think about the future of global politics? Overall, these provide us with a diverse, stimulating, and thought-provoking set of essays about the future of global politics, as both discipline and set of empirical problems.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".