符以载道:北美图书馆拼音转换廿周年口述访谈及文献资料汇辑 / Celebrating 20th Anniversary of the Pinyin Conversion Project in North American Libraries: A Collection of Interviews, Documents, and Publications
Bibliographic record
Abstract
2020年,本书编者开展了回顾和纪念北美图书馆汉语拼音转换项目二十周年活动,对当年参与拼音转换工作的部分图书馆员进行了线上口述访谈。这里,我们将完整的口述访谈录及拼音转换相关代表性文献编辑成这本《符以载道:北美图书馆拼音转换廿周年口述访谈及文献资料汇辑》。我们旨在以此书纪念汉语拼音转换完成二十周年,并向参与转换的前辈同仁们致敬;并真诚地希望本书能促进我们回顾、学习和重新思考世纪之交北美东亚图书馆界的拼音转换历史,并在信息技术迅速发展、图书编目规则和系统不断变化的新环境中,不辍传承,勇于创新,继往开来。 In 2020, we initiated an oral history project to commemorate the 20th anniversary of the Pinyin Conversion Project, a collaborative effort to standardize the Romanization of Chinese characters in North American libraries. We interviewed librarians who actively participated in and contributed to the project and compiled their stories and diverse documents into this volume. This book serves as a tribute to our colleagues and a valuable resource for understanding the challenges, opportunities, and implications of the Pinyin conversion project. We aspire for it to inspire librarians to reflect on the past and future of Chinese cataloging and Chinese Studies librarianship amid the evolving digital age and complexities of national and international dynamics.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".