Bibliographic record
Abstract
While prejudice against Jews is a real and ongoing category in Western culture, little attention has been paid to the myths of the Jews' and their impact in countries outside the West. This work draws on a wide variety of source materials from the past two centuries to examine the images of the Jews' as constructed in China. However, the interest here does not lie in the determination of the boundary between the real and fictional aspects of these images. Rather, it lies in the implications associated with the Jew' as an other', which remains a distant mirror in the construction of the self' amongst various social groups in modern China. Although it has been noted by a few scholars that the use of the Jews' as a category was important to many thinkers of modern China in the construction of their nationalistic and socio- political ideologies, this is the first systematic study in the field to be published. This book is also more than a historical book on China in that it opens a new arena for modern Jewish studies from a unique angle.
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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 | 0.000 | 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".