A comparison of foreign authorship distribution in <i>JASIST</i> and the <i>Journal of Documentation</i>
Bibliographic record
Abstract
Abstract This article reports findings from a study of the geographic distribution of foreign authors in the Journal of American Society for Information Science & Technology (JASIST) and Journal of Documentation (JDoc). Bibliographic data about foreign authors and their geographic locations from a 50‐year publication period (1950–1999) are analyzed per 5‐year period for both JASIST and JDoc. The distribution of foreign authors by geographic locations was analyzed for the overall trends in JASIST and JDoc. UK and Canadian authors are the most frequent foreign authors in JASIST. Authors from the United States and Canada are the most frequent foreign authors in JDoc. The top 10 geographic locations with highest number of foreign authors and the top 10 most productive foreign authors were also identified and compared for their characteristics and trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.058 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".