Critical Han Studies Conference Report
Bibliographic record
Abstract
Last weekend (April 24-27), I and about 70 other students, scholars, and members of the public attended the Critical Han Studies conference held at Stanford University. Organized by Tom Mullaney of Stanford and China Beat, Jim Liebold of La Trobe University, Stéphane Gros of Centre National de la Recherche Scientifique, and Stanford PhD student Eric Vanden Bussche, the conference drew scholars from around the world—China, Hong Kong, Taiwan, Japan, England, France, Belgium, Australia, Canada, and the U.S.—and from a wide variety of disciplines: history, anthropology, religious studies, literature, East Asian Studies, etc. Most importantly, it was a lot of fun. With over 40 presenters, this event was a successful kick-off for a new subfield in China studies: Critical theories of Han-ness. Like critical theories of Whiteness as an invented racial category which shifts over time, Critical Han studies will cast an analytic eye on China’s racial majority. Given that roughly one in five people on earth could claim Han Chinese identity, this is a Herculian—or shall we say Panguvian—task, and the work has only just begun.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.195 | 0.026 |
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".