Bibliometric Analysis of the <i>Journal of Cross- Cultural Psychology</i> During the First Ten Years of the New Millennium
Bibliographic record
Abstract
A bibliometric analysis of research articles published in the Journal of Cross-Cultural Psychology ( JCCP) during the first 10 years (2001-2010) of the new millennium was provided. There were 457 original research articles, which were cited 6,187 times in 4,227 citing papers (January 25, 2012). Although the largest number of articles were authored by researchers from the United States (52.3%), Canada (12.0%), and People’s Republic of China (11.6%), the highest impact articles were written by Israeli (30.5 citations per article), Estonian (29.5), and Swiss (23.6) psychologists. The country self-citation rates or biases were highest in the United States (+22.9%), the Netherlands (+20.7%), and People’s Republic of China (+20.5%), showing that the small-world networks operate most strongly in these three countries. As revealed by a cross-journal citation pattern, JCCP had the strongest influence on personality and social psychology research and negligible on intelligence and cognitive research. The impact of the research articles published in JCCP on the core psychology journals remained at the same (modest) level, while the journal self-citation bias demonstrated a slight increase during the last 10 years.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.144 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".