International Collaboration for Academic Publication
Bibliographic record
Abstract
In this study, the authors examine factors that explain international scholars’ success in publishing in North American management journals through collaboration. Drawing on the international entry mode literature, the authors propose that international collaboration teams are more successful when they increase complementary resources and reduce transaction costs. A sample of 364 articles from 10 North American management journals shows that teams published in higher impact management journals when they had U.S. or Canadian collaborators, higher proportions of assistant professors, and less gender diversity. Combining additional findings from 23 semistructured interviews, the authors provide a research model to explain the resources and costs embedded in international collaboration teams as well as mechanisms that help transform costs into resources.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.031 | 0.015 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.100 | 0.026 |
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".