Qualitative Study on Dimensions of 'Centre of Excellence' of Foreign Subsidiaries: A Content Analysis of Scientific Literature by Nvivo (Étude Qualitative Sur les Dimensions du Concept de Centre de L’Excellence de la Filiale a L’Étranger: Une Analyse du Contenu de la Littérature Scientifique par Nvivo) (French)
Bibliographic record
Abstract
By using a qualitative research approach and based on content analysis of scientific articles which consider “foreign subsidiary” as “Centre of Excellence” by multinational companies in the USA, Canada and Europe, this paper tries to explore “Centre of Excellence” concept by trying to identify the main dimensions which define it by using different data analysis methods which were used for research on this topic. At first stage, systematic literature review will highlight the definition of the “Centre of Excellence” of the foreign subsidiary, taking into consideration the dimensions which were studied on this topic in the academic articles published between 1995-2010. This stage will take the researchers to choose, based on pre-established criterias, sample of 98 articles. These articles are then used for codification by Nvivo software for qualitative research. A step of triangulation will follow and will have the results as principal categories which define the “Centre of Excellence” at subsidiary level. The last section will deal with results and conclusions along with the research limits and future possibilities.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".