The Emergence and Impact of MNC Centres of Excellence: A Subsidiary Perspective
Bibliographic record
Abstract
INTRODUCTION AND OVERVIEW PART 1: THE CENTRES OF EXCELLENCE PROJECT The Centres of Excellence Project: Methods and Some Empirical Findings PART 2: DETERMINANTS AND BASIC FEATURES OF CENTRES OF EXCELLENCE Development of MNC Centres of Excellence Subsidiary Influence and Corporate Learning: Centres of Excellence in Danish Foreign-Owned Firms The Impact from Business Networks on MNC Competence Development: A Case Study PART 3: DEVELOPMENT OF SUBSIDIARY COMPETENCE Industrial Clusters and Foreign Companies Centres of Excellence in Norway Multinational Research Subsidiaries in Denmark R & D Centres of Excellence in Canada The Competence of Formally Appointed Centres of Excellence in the UK Competence Creation and Recognition: A Case Study PART IV: USE AND INTEGRATION OF SUBSIDIARY COMPETENCE WITHIN THE MNC Mergers and Acquisitions as Establishment Modes for Centres of Excellence: The Case of Italian Subsidiaries Characteristics of R & D Centres of Excellence in MNCs The Impact of Centres of Excellence on MNC Performance The Dilemma of Developing a Centre of Excellence: The Case of ABB Generation
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".