Living and working globally
Bibliographic record
Abstract
Management challenge Living and working globally is both exciting and routine. It is both easy and difficult. Why? Because some people initially bring more skills to global assignments than others – that is, some have less to learn – and because some foreign locations are more comfortable or familiar than others. For example, a manager from Singapore would likely have an easier time moving to the United States or United Kingdom than Ecuador or Peru, because more Singaporeans speak English than Spanish. This does not suggest that they should avoid South America; they just have to work harder, as the territory is less familiar. Moving overseas brings with it a number of challenges, including both psychological and socio-cultural adjustments. In addition, there are personal, time, family, and career considerations. There is also the problem of returning home following the assignment. All of this is doable, of course, but it is made much easier to the extent that managers can develop and enhance their multicultural competence. Chapter outline Global assignments page 366 Challenges of living and working globally 373 Adapting to local cultures 379 Managing repatriation 394 Manager’s notebook: Living and working globally 396 Summary points 401 Applications 11.1 Global assignments at Shell 370 11.2 Wei Hopeman, Citi Ventures 374 11.3 Preparing for global assignments 378 11.4 Dining out in Luogang 380 11.5 Mr. Smith and Mr. Zhang 392 11.6 Andrea Walker, returning home 395 There are no foreign lands. It is the traveller only who is foreign. Robert Louis Stevenson Poet and novelist, Scotland Everyone thinks in terms of changing the world, but no one thinks in terms of changing himself. Leo Tolstoy Poet and novelist, Russia
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.040 |
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".