The changing landscape of JIBS authorship
Bibliographic record
Abstract
In this study, we examine the landscape of JIBS authorship over time to assess: (1) the accessibility of JIBS to new contributors, and (2) the diversity of authors contributing to JIBS. Our analysis of author data from 1972 to 2014 shows that JIBS is becoming more accessible, as indicated by the high and sustained proportion of first-time contributors to the journal. This is also evident from the recent decline in the share of authors with multiple past JIBS publications. With regard to diversity, our findings show that JIBS has a much wider geographic scope of authors on its landscape in comparison to previous decades. This may be attributed partly to increasing travel and communication in scholarly communities, and partly to the increased migration of scholars in the recent decades. Our analysis of migration patterns of JIBS authors suggests that about 51 % of prominent international business scholars are employed outside their country of birth. Of the 49 % employed in their country of birth, 12 % are return migrants. In our sample, China, South Korea and Canada have the highest number of returnees. The USA, the UK, Germany, the Netherlands and China have the highest number of natives, whose country of birth, country of PhD-granting institution and country of university affiliation are identical.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".