Location, Competitiveness, and the Multinational Enterprise
Bibliographic record
Abstract
Abstract This article provides an overview of the key insights resulting from recent international business research on the interactions between location advantages and the competitiveness of multinational enterprises (MNEs). It consists of four main sections. First, the evolution of the location advantage concept in the international economics literature is discussed. Here, it appears that the international economics literature has substantially broadened its analytical scope in the last few decades. However, the field of international business research had gone even further in its analysis of the interactions between location and MNE competitiveness because of its in-depth focus on the actual behaviour of MNEs. The complex nature of location advantages for MNEs is discussed in more detail in the second section. The third section describes the intellectual foundations of a spatial analysis of MNE activities. Finally, the fourth section discusses the relative contribution of home country specific advantages (CSAs) and host CSAs to MNE competitiveness.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".