The Securitization of Transborder Ethnic Kinship: Contextual Explorations around the World
Bibliographic record
Abstract
In the decade since the start of Russia's irredentist attack against Ukraine under the pretext of defending “compatriots” in neighboring states (Putin 2014), transborder ethnic ties have again found themselves at the forefront of debates on international security. Amidst widespread speculation that other countries with Russophone minorities might be next on Russia's list (Trimbach and O'Lear 2015), fears have also grown that China could follow this example and seek to realize its long-standing claim to Taiwan. These are just two prominent examples within a global context marked by increased tensions around what is today commonly labeled “kin-state politics” (Waterbury 2020).1 These tensions, in turn, reflect wider uncertainties about the sustainability of the liberal international order established following the end of the Cold War: with the European Union (EU) currently beset by internal challenges from sovereigntist and ethnopopulist movements propounding exclusivist nationalism (Brubaker 2017; Jenne 2018), questions also abound concerning the US commitment to the North Atlantic Treaty Organization (NATO) and international security and the likely global impact of rising powers such as China and India.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".