Brief report: Publications from mainland China, Hong Kong, and Taiwan in behavioral journals 1980–2021
Bibliographic record
Abstract
Abstract Research involving international research communities has been advocated in the field of behavior analysis (Dymond et al., 2000; Martin et al., 2016). The purpose of the present study was to report the status of behavioral research in mainland China, Hong Kong, and Taiwan, in terms of number of publications, types of research, and frequency of collaboration with international researchers. Fifteen behavioral journals were selected from the list by Cooper et al. (2020). These were searched by hand to find publications conducted in or authored by researchers from mainland China, Hong Kong, and Taiwan dating from each journal's inception to December 2021. The earliest publication we found appeared in 1980 in The Psychological Record. Over the following four decades (1980–1989; 1990–1999; 2000–2009; 2010–2021), the number of publications per decade increased dramatically and continues in recent years to rise. Publications include research reports, review papers, and conceptual articles, with the majority being basic research reports published in Behavioral Processes. Approximately half the publications involve collaboration with international researchers, mostly in North America. Implications for behavioral research, practice, and policy in mainland China, Hong Kong, and Taiwan are discussed.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.043 | 0.057 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".