Stories of world Politics: Between History and Fiction
Bibliographic record
Abstract
The discipline of International Relations is defined by a fundamental tension between history and fiction. On the one hand, historical approaches are crucial in tracing the origins of international order, contextualising key concepts, and uncovering marginalised voices. On the other, the discipline is invariably caught in its own mythologies, which shape what is “real” and “true” in global politics. Instead of treating the ambiguous distinctions between history and fiction as problems to be definitively resolved, this Special Issue takes up these tensions as generative for understanding, manoeuvring, and even disrupting and (re)imagining the field of IR. Examining these dynamics from several perspectives – from the writing of history to the political force of fictional imaginaries – the articles in this Issue show that the relation between history and fiction is a constitutive force of the international, in both theory and practice. Bringing together historical and critical perspectives, the Issue elucidates how ideas of history and fiction, reality and storytelling, facticity and imagination shape and are in turn shaped by world politics. Historical approaches to IR, we argue, can do much more than adjudicate between these amorphous categories. Here, we pursue the possibilities.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".