Leadership in the Academic Field of Business and Management and the Question of Country of Origin: A Commentary on Burgess and Shaw (2010)
Bibliographic record
Abstract
With globalization in business academia expanding and deepening, it is timely to question the validity and utility of the concept of country of origin as a base category for comparative cross‐cultural research and theory development. In their contribution in the British Journal of Management, Burgess and Shaw (2010) rank the most productive institutions and countries contributing to board membership of top ranked journals on the basis of their country of origin. Taking their findings as a starting point for our discourse, we re‐analyse their database, in addition to our own investigations. We contend that while country of origin may be an appropriate category for between‐countries comparison of multinational entities, it is of little use when comparing meta‐national institutions, such as top tier refereed journal boards and the globalized business/management schools from which they are drawn. Our findings point towards the need for finer differentiation of what constitutes the concept country of origin, but also that its relevance should be questioned in, at least, globalized contexts. The question we pose extends to any pertinent ‘globalized’ topic within and without business and management.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.039 | 0.048 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".