Bibliographic record
Abstract
Identities are made, not born. Although I claim no originality for this insight, it is striking how much our understanding of the world has continued to be anchored on the premise that identities – in particular racial, ethnic, and national – are self-evident. In the context of China, not only do we often learn from textbooks and popular media that the country has had a continuous history of over five thousand years (a “fact” that has been used to show that China is either steady or stodgy), we are also constantly reminded by official propaganda and well-intentioned observers alike that the Chinese nation ( Zhonghua min zu ), internally diverse as it might be, is ultimately united by blood as the descendant of the Yellow Emperor. Although the optimistic scholar might view such efforts to promote an essential Chinese identity as so transparent as to be unworthy of intervention, it remains the case that, despite all the harms that have been done in the name of racial, ethnic, or national unity, we who live in the new millennium are still very much, in the broadest sense of the term, prisoners of modernist identities. To claim that identities are constructed is not to deny that they could be deeply meaningful. Rather, it is to insist that, in order to capture more fully the complexity of the human past, we must approach the formation of identities not as an aside but as an essential component in historical inquiries.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.303 | 0.150 |
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".