Bibliographic record
Abstract
A Note on Romanization F ollowing the current trend of scholarship, this book adopts the pinyin system of transliteration for Chinese personal names, places, and special phrases.To respect Chinese customs, the surname appears before the given name.In the bibliography, however, the surname is provided fi rst but is not separated from the given name by a comma.For Chinese-American or Chinese-Canadian names, their surnames appear after their given names, just like other American or Canadian names.Special phrases are transliterated in a combination of a few Chinese syllables.For example, Qieguodadao appears as one phrase, instead of four separate syllables.For Chinese publishers, their pinyin names are rendered without further translation.Almost all Chinese names are romanized in pinyin , but some long-accepted Wade-Giles versions are retained; notably, "Sun Yat-sen" rather than the pinyin "Sun Zhongshan" and "Chiang Kai-shek" instead of "Jiang Jieshi."Traditional terms are kept, such as " ginseng" rather than the Mandarin " renshen ."Occasionally, the name of a famous personage is provided in pinyin followed by its original Wade-Giles version in brackets."Yuan Shikai," is spelled according to the pinyin system, rather than the Wade-Giles "Yuan Shih-kai," or another variant "Yuan Shi-k'ai," which were the two spellings known to the world when he was president of China.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.019 |
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".