Bibliographic record
Abstract
In this chapter, we will look in greater detail at each of the seven concepts of this book's unifying framework. The reader will recall the seven concepts, shown again in Figure 1.1: 1. Internationally transferable (or non-location-bound) firm-specific advantages (FSAs) 2. Non-transferable (or location-bound) FSAs 3. Location advantages 4. Investment in – and value creation through – recombination 5. Complementary resources of external actors (not shown explicitly in figure) 6. Bounded rationality 7. Bounded reliability Let us start by discussing internationally transferable FSAs. Internationally transferable FSAs and the four MNE archetypes The MNE creates value and satisfies stakeholder needs by operating across national borders. When crossing its home country border to create value in a host country, the MNE is, almost by definition, at a disadvantage as compared to firms from the host country, because these firms possess a knowledge base that is more appropriately matched to local stakeholder requirements. The MNE incurs additional costs of doing business abroad, resulting from cultural, economic, institutional and spatial distance between home and host country environments. MNE managers often find it particularly difficult to anticipate the liability of foreignness resulting from the cultural and institutional differences with their home country environments, even though these may be reduced over time as the firm learns and gains increased legitimacy in the host country. In order to overcome these additional costs of doing business abroad, the MNE must have proprietary internal strengths, such as technological, marketing or administrative (governance-related) knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".