TRANSATLANTIC AFFILIATIONS OF SCIENTIFIC COLLABORATION IN STRATEGIC MANAGEMENT: A QUARTER-CENTURY OF BIBLIOMETRIC EVIDENCE
Bibliographic record
Abstract
The main objective of the paper is to identify and explore patterns and dynamics of transatlantic scientific collaboration in the field of strategic management between the United States (US) and European countries (EUC) during the last quarter century. Scholarly connections between countries, cities and institutions on the basis of co-author affiliations were analysed to determine the knowledge flow from a geographical perspective. This is the first time international scientific collaboration between researchers in the field of strategic management has been studied to such an extent. We employed all sources of relevant data from the Web of Science and Scopus databases and explored 453 results. Utilizing a bibliometric analysis, our study offers a comprehensive and up-todate identification and assessment of the current situation and dynamics of transatlantic scientific collaboration. The obtained results confirm the dominant role of the US in this type of collaboration. Also, the dominant role of several clusters in terms of collaboration, both on country and institution levels can also be observed. The study confirms the weaker position of Eastern and Central Europe countries in this collaboration and provides some recommendations to increase this type of knowledge exchange in the future.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.084 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".