Bibliographic record
Abstract
This chapter focuses on expatriate managers and examines Black and Gregersen's idea that, when it comes to successfully managing expatriate managers, there are three best practices: ‘[Successful companies] focus on creating knowledge and developing global leadership skills; they make sure that candidates have cross-cultural skills to match their technical abilities; and they prepare people to make the transition back to their home offices’. In theory, expatriation is supposed to, inter alia , produce managers who have an in-depth knowledge of the MNE, understand the pressures leading to benevolent preference reversal in subsidiaries and can integrate geographically dispersed operations. These ideas will be examined and then criticized using the framework presented in Chapter 1. Significance MNEs must develop managers with a broad mental map covering the entirety of the MNE's geographically dispersed operations. This is critical to the MNE's long-term profitability and growth, especially in an era when foreign markets are becoming increasingly important contributors to innovation and cost reduction at the upstream end of the value chain, and to overall sales performance at the downstream end. In fact, managers commanding deep knowledge of internal MNE functioning – including the challenges of simultaneously addressing legitimate business objectives/interests at multiple geographic levels within the firm – represent the MNE's key resource to facilitate international expansion and to coordinate geographically dispersed, established operations. Such managers are best positioned to (a) engage in the international transfer of non-location-bound FSAs from the home nation; (b) identify the need for new FSA development in host countries and facilitate such development; and (c) meld both location-bound and non-location-bound FSAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".