Bibliographic record
Abstract
Abstract The period since about the late 1980s has witnessed the phenomenal ascent of the People’s Republic of China as a political, economic, and military power on the global stage. China’s rise has engendered an earnest, if perhaps not well-executed, agenda to promote a more attractive image of the country. In this period China has also experienced a rapid escalation in outbound migration to various parts of the world, with a small number of countries in the global West emerging as the preferred destinations for Chinese migrants, and, in some cases, China becoming their biggest source of new migrants. In the United States, China replaced Mexico as the top sending country in 2018. In Canada, mainland China has taken over from Hong Kong and Taiwan as the largest source of Chinese immigration, while in Australia, China now has the second-largest migrant population behind the United Kingdom, and has only recently slipped into second position behind India as the nation’s leading source of new immigrants. These developments have made China’s diaspora the biggest in the world. In the eyes and minds of the Chinese government, Chinese migrants are important potential assets in its efforts to push its global soft power agenda. The period of accelerated outbound migration from China coincided with the emergence of first the internet, and then digital media—in particular, the most popular Chinese social media platform, WeChat (Weixin in Chinese). Against the backdrop of these developments at the macro level, the topic of social media and the Chinese diaspora becomes a question of considerable significance. Some analysts argue that the dramatically enlarged mainland Chinese diaspora has effectively become an instrument of China’s soft power agenda, while others point out the positive role that members of this group play in their host communities. In particular, they highlight the potential of Chinese-language social media—and in particular WeChat, which is widely used by Chinese people both within and outside China—to have a beneficial impact on Chinese immigrants’ prospects for social integration in the countries where they now reside. The pursuit of these questions entails a brief foray into a number of research areas, including the Chinese diaspora, the history and transformation of Chinese-language diasporic media, the infrastructural and regulatory framework of WeChat, and public diplomacy via diaspora. Addressing these questions also has the benefit of broadening, and possibly enriching, the concepts of digital diaspora, on the one hand, and digital citizenship, on the other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".