Bibliographic record
Abstract
Why should we study international relations? What ought to be the purposes of this field of study? Is it sufficient to be a group of scholars animated by nothing more than our idiosyncratic intellectual curiosities? Or does ‘science for science's sake’ constitute a compelling rationale for our endeavours? Are there deeper purposes than these that should, or perhaps do, animate our research, teaching and public engagement? And if there are such purposes, what are their implications for how we imagine International Relations (IR) as a field of inquiry, and for how we go about our scholarly pursuits? In the following pages I make the case for one possible answer to these questions, that International Relations ought to be concerned first and foremost (though not exclusively) with the praxeological question of ‘how should we act?’ that animates this volume. It should confront directly, and unashamedly, the most challenging issues of human action in a globalising world, a world characterised by political convergence and division, cultural homogenisation and diversification, economic growth and persistent inequality, and the transformation of organised violence. It is a purpose rooted in the origins of our field, one that its early architects took as axiomatic, one that E. H. Carr saw as the mark of a ‘mature science’ of International Relations. It is also a purpose still animating the field, though in a subterranean fashion, as much forgotten as denied.
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 | 0.039 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.151 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| 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".