Research on Transnational Ethnic Relations : World Context and Cases in Vietnam
Bibliographic record
Abstract
Transnational ethnic relations (TER) are the relations between ethnic groups, living internally in one country and their coethnics or counterparts of other ethnic minorities, living abroad in other countries that share or do not share common borders. For a multi-ethnic country, TER play a very important role, affecting national socio-economic development and security, since being external relations with counterparts living abroad, but they have profound internal impacts on live of the country. So far, there have been various TER researches conducted by scholars in all over the world. Concerning the studied concepts and theories, the most notable are: the concepts of fluid border, soft border, social space border, the theories of diaspora and neoliberalism. In accordance with these concepts and theories, the authors have carried out the application researches on transnational migration, marriage, religious belief, culture, politics, smuggling, crime. In Vietnam, a country with 46 ethnic groups having coethnics inhabiting in various countries, under the influence of historical background, globalization and the Doi Moi (the Innovation policy of Vietnam), TER have been developing significantly, especially since 1986. This relationship has been expressed in numerous fields, such as politics, economy, society, culture and national security; in the majority Kinh and other ethnic minorities. This relationship relates to, not only 3 countries bordering Vietnam, such as China, Laos and Cambodia, but also to other countries: USA, Canada, France, Australia, Korea, Thailand, Malaysia, Indonesia... that do not share common borders with Vietnam, as well. TER researches in Vietnam reveal that, economic TER are the most exposed, via such as aspects: labor migration, trade, cross-border business, especially those activities carried out in the areas along Vietnam-China, Vietnam-Laos and Vietnam-Cambodia borders. Concerning the social TER, transnational marriages have been increasing, since trend of getting married to coethnics is preferred, especially in ethnic minorities. On cultural relations, festival and religious activities have connected many ethnic minorities in Vietnam with their overseas coethnics and counterpart who are practicing the same religion. Meanwhile, TER have also resulted in some illegal activities, such as cross-border smuggling, drug or women trafficking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".