Ersatz Twin City Formation? The Case of Blagoveshchensk and Heihe
Bibliographic record
Abstract
Border studies posit that twin cities represent aspirations to proactively and deliberately become one entity resulting in fundamental changes to the social, economic and political identity of the two communities involved. While some twin cities in the EU and between EU and Russia do follow this pattern, this is not the case in the Russian Far East. The cities of Blagoveshchensk (Russia) and Heihe (China) claim to be twin cities even though they have deliberately confined their twinning activities to trade and tourism and specifically excluded any change that may involve administrative boundaries or national security. Based on interviews and examination of public documents and relevant studies, we argue that the case of Blago and Heihe can be characterized as ersatz in the sense that their city twinning effort is deliberately compartmentalized. We argue that this approach is the local officials’ response to obstacles generated by nationalism, history, vast divergence in economic trajectory and population trends, and close scrutiny by respective national governments. In spite of these, the two cities regard twinning as a valuable marketing tool and have progressively, though asymmetrically, adopted this brand. We further argue that pursuing the rhetoric of city-twinning allows the officials to advance claims to opportunities in trade and tourism and potential access to additional national resources without tackling the difficult questions of more comprehensive cross-border interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".