In the beginning: The International Relations enlightenment and the ends of International Relations theory
Bibliographic record
Abstract
The question of endings is simultaneously a question of beginnings: wondering if International Relations is at an end inevitably raises the puzzle of when and how ‘it’ began. This article argues that International Relations’ origins bear striking resemblance to a wider movement in post-war American political studies that Ira Katznelson calls the ‘political studies enlightenment.’ This story of the field’s beginnings and ends has become so misunderstood as to have almost disappeared from histories of the field and accounts of its theoretical orientations and alternatives. This historical forgetting represents one of the most debilitating errors of International Relations theory today, and overcoming it has significant implications for how we think about the past and future development of the field. In particular, it throws open not only our understanding of the place of realism in International Relations, but also our vision of liberalism. For the realism of the International Relations enlightenment did not seek to destroy liberalism as an intellectual and political project, but to save it. The core issue in the ‘invention of International Relations theory’ — its historical origins as well as its end or goal in a substantive or normative sense — was not the assertion of realism in opposition to liberalism: it was, in fact, the defence of a particular kind of liberalism.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".