Bibliographic record
Abstract
The history of China can no longer be innocently a history of the West or the history of the true China. It must attend to the politics of narratives – whether these be the rhetorical schemas we deploy for our own understanding or those of the historical actors who give us their world. Prasenjit Duara, Rescuing History from the Nation The practice and process of identifying and categorizing the border population in China have persisted – albeit in different contexts – through the Qing to the modern period. In late imperial times, the demarcation of the borderland population was clearly grounded on the desire of the centralizing state to extend control as well as on the imperial rhetoric of “transformation through submission” ( gui hua ). Although Qing-dynasty emperors were in general more concerned with the regions corresponding to present-day Mongolia, Xinjiang (Eastern Turkestan), and Tibet than with the border zone in the south, they did find it important to catalog the different peoples under their rule as well as to ascertain the degree of transformation or submission of individual populations. In the Republican period, by contrast, revolutionary and intellectual leaders were evidently less interested in sustaining an empire than in the creation of a modern nation-state. Although they did much to identify and categorize the “non-Han” peoples, most members of the elites were ultimately concerned with how and when the “non-Han” would be transformed through assimilation ( tong hua ) or enlightenment ( kai hua ).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".