Bibliographic record
Abstract
Abstract If we were to ask the average person on the street to share the first idea that pops into their head when they think of the wide world, it seems a safe bet that the multiplicity of languages would be a popular response. And yet, in the discipline of International Relations (IR) this basic fact has traditionally drawn surprisingly little attention. Why has the discipline been so impervious to global language politics, when it is arguably at the very core of international life? This paper seeks to explain and redress the field’s hearing impairment by showing how languages matter to global politics, why they have been mostly ignored so far, what the challenges are in studying them, and where the field may take this innovative research program moving forward. First, a case is made that modern sovereignty rests on linguistic territoriality and that the liberal international order is enabled by English as a global lingua franca. Then, key impediments to taking interlingual relations seriously are explored, including Anglo-American dominance, the pervasive myth of Babel, and an incomplete linguistic turn. Looking ahead, analytical and normative challenges are addressed, including reification, reductionism, translation, and diversity. The paper concludes by laying out a detailed research agenda spanning a variety of topics and sub-fields in IR and beyond. Overall, grasping the global politics of languages opens a unique window onto the international and its manifold social forces.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".