The Emergence and Impact of MNC Centres of Excellence: A Subsidiary Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it