Chinese Reading Development in Monolingual and Bilingual Learners: Introduction to the Special Issue
Bibliographic record
Abstract
This special issue was inspired by the Research in Reading Chinese Conference held at the Ontario Institute for Studies in Education, the University of Toronto, in 2010 to celebrate Dr. Richard C. Anderson's distinguished research career and his pioneering role in Chinese reading research. The five articles presented in this special issue cover topics related to the Chinese reading development of monolingual and bilingual learners. They include similarities and differences between Chinese and English in reading development and processes (Perfetti, Cao, & Booth, 2013 Perfetti, C., Cao, F. and Booth, J. 2013. Specialization and universals in the development of reading skill: How Chinese research informs a universal science of reading. Scientific Studies of Reading, 17: 5–21. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and reading problems (McBride-Chang et al., 2013 McBride-Chang, C., Shu, H., Chan, W., Wong, T., Wong, A. M.-Y., Zhang, Y. and Chan, P. 2013. Poor readers of Chinese and English: Overlap, stability, and longitudinal correlates. Scientific Studies of Reading, 17: 57–70. [Taylor & Francis Online] , [Google Scholar]), the role of visual-perceptual and visual-orthographic skills in Chinese reading (Anderson et al., 2013 Anderson, R. C., Ku, Y.-M., Li, W., Chen, X., Wu, X. and Shu, H. 2013. Learning to see the patterns in Chinese characters. Scientific Studies of Reading, 17: 41–56. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Luo, Chen, Deacon, Zhang, & Yin, 2013 Luo, Y. C., Chen, X., Deacon, S. H., Zhang, J. and Yin, L. 2013. The role of visual processing in learning to read Chinese characters. Scientific Studies of Reading, 17: 22–40. [Taylor & Francis Online] , [Google Scholar]), and the influence of first-language semantics on second-language lexical learning (Saji & Imai, 2013 Saji, N. and Imai, M. 2013. Evolution of verb meanings in children and L2 adult learners through reorganization of an entire semantic domain: The case of Chinese carry/hold verbs. Scientific Studies of Reading, 17: 71–88. [Taylor & Francis Online] , [Google Scholar]). The findings of these articles highlight the contribution of Chinese reading research to the scientific study of reading.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".