Location Choice Network Patterns of Japanese Multinational Companies in Europe
Bibliographic record
Abstract
This research investigates network patterns of location choice of multinational companies by using multinomiallogit method. It empirically analyses regional economic factors, which were significant for attracting investmentsof Japanese companies during the last decade, by using the most detailed regional data possible. In addition toprevious studies, this paper particularly addresses factors, which follower Japanese companies consideredimportant in their investment decisions. For Japanese multinational company to locate near to other alreadyestablished company from the same country there could be such reasons as: they tend to follow their businesscustomers or because of existing intra-firm linkages already established in Japan, which they carry on in theirinvestment decisions.The aim of the paper is threefold. Firstly, it analyzes significant regional economic factors, which followerJapanese companies consider important in choosing regions with already established Japanese firms and,secondly, it analyzes those regional economic factors, which are significant for those companies, which chooseto locate near to hubs of other Japanese companies. Thirdly, by using distances between regional centers, thispaper tries to establish significance of physical distance in establishing hub of Japanese companies. Paperhypotheses that Japanese companies disregard geographical distance in their investment decisions as they createnetworks of Japanese companies.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".