Localization in World Politics: Bridging Theory and Practice
Bibliographic record
Abstract
Abstract This Introduction and Special Forum highlight the importance of localization for the study of world politics, both as a theoretical concept in international relations research on norms and as a set of practices and policies. The article tackles four questions: (1) Why has localization become a focus of scholarly and policy attention? (2) What are the historical precursors of localization? (3) What is being “localized” and who/what is “local”? And (4) how can localization be studied (i.e., using which methods and approaches)? After unpacking common functionalist, normative, and strategic arguments in favor of localization and situating the concept historically, we develop a novel relational conception of localization as both a process and an outcome. Our central objective is to bridge the diverse meanings and uses of the term that exist across theory and practice. Drawing on interdisciplinary perspectives and empirical examples from forced migration, humanitarianism, the protection of civilians, transitional justice, and Women, Peace and Security, we consider key dilemmas and challenges facing both the academic study and practice of localization and identify several methods and approaches that can be used to analyze this important topic in world politics.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.009 | 0.086 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".