S<scp>helly</scp> C<scp>han</scp>. <i>Diaspora’s Homeland: Modern China in the Age of Global Migration</i>.
Bibliographic record
Abstract
In recent years, there has been a surge in interest in the history of the Chinese diaspora, sparked no doubt at least in part by the growing strength of China on the world stage. Studies of the Chinese abroad often center on the migrants themselves: their lived experiences, their agency in choosing and shaping their lives, and their influence on the societies in which they’ve chosen to live. According to Shelly Chan, not enough attention has been paid to the simple question of how their migrations have changed China. In Diaspora’s Homeland: Modern China in the Age of Global Migration, Chan offers a partial remedy to this problem. She introduces her conception of diaspora time—a temporality unbounded by the traditional periodization of China’s political history, but which marches on through the lives and families of migrants. Within the long narrative of diaspora time, she explores a series of five “diaspora moments,” or points at which diaspora time intersects with China’s narrative in ways that foment meaningful change. Her study is well grounded by research in libraries and archives in southeastern China as well as in Hong Kong, Taiwan, Singapore, the United Kingdom, and Canada. However, Chan’s aim is not to introduce a wholly new history of migration and the diaspora, but to offer a reinterpretation of key episodes that answer the core question about the importance of diaspora to China’s twentieth-century transformations. As she notes, “Far from being insular and fixed, diaspora is a recurrent dialogue about Chinese connections in the world” (106). That dialogue continued alongside such events as the 1911 revolution, the Sino-Japanese wars, and the communist revolution, acting as an ever-evolving renegotiation between migrants and the state.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".