Bibliographic record
Abstract
More than thirty million people of Chinese descent live in over one hundred different countries and territories as migrants, settlers and sojourners. Most would qualify to be part of the diaspora and, insofar as people identify themselves as Chinese or have that identity ascribed to them, it can be said that they are bearers of Chinese diaspora cultures. The assumption that they all have something in common stems from their origins, whether distant or recent, in China. But some of them left China only recently while others have ancestors who have lived outside China for generations, often in different countries and even on different continents. Thus it would be misleading to think of them as having much in common. Also, Chinese people settled in lands as far apart as Indonesia and Canada, Tahiti and the Netherlands. To suggest that the Chinese who have lived among such different host peoples share the same culture would be unwarranted. However hard some Chinese might have tried to stay the same wherever they went, evidence can be found that some of them have distinct cultures. Some are complex and interconnected cultures that have grown over centuries in different communities while others are peculiar to small clusters of families that still claim to be Chinese.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".