Bibliographic record
Abstract
International Relations (IR) theorists have increasingly embraced global history to correct disciplinary myths and develop more inclusive theories of world politics. While this expansion promises to broaden the theoretical foundations of the field, we argue that it increases the risk of structural anachronism—the embedding of later-developed knowledge into the very construction of the historical record. Drawing on insights from historical methods, archival sciences, and the philosophy of history, we show how two retrospective processes—information-destroying, which shapes what is preserved, and information-obscuring, which governs how that information is organized—flatten ideational variation in the historical record and distort the evidentiary foundations on which scholars depend for testing and building theories of world politics. These distortions impact both positivist and interpretivist approaches to global history, leading scholars to make false positives, project coherence onto fragmented pasts, and underestimate the degree of historical change. As a result, efforts to globalize IR may reproduce the disciplinary myths and conceptual blind spots it aspires to overcome. We illustrate these dynamics through recent research on war and international order, and conclude with a paradox for IR theory’s relationship to history: As theory evolves, it can obscure the past by altering the very record it seeks to explain. This paradox calls for greater reflexivity about the epistemic costs of theorizing from structurally distorted records.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".