Cross-cultural professional experiences of female expatriates
Bibliographic record
Abstract
Purpose In this world of global interconnectedness, women continue to develop cross-cultural careers and their experiences impact global scholarship and practice. The purpose of this paper is to explore the relationships, resources and characteristics that support female expatriate success, with specific focus on the role of mentor/coach relationships. The sample included 102 women from the USA, Canada, Australia and the UK working or formerly working in Mainland China, Hong Kong, Macau or Taiwan. Design/methodology/approach This three phase sequential mixed-methods exploratory research study included 10 one-on-one semi-structured interviews, 102 survey respondents and 3 facilitated focus groups attended by nine professional women. Findings This research offers evidence that resiliency-based characteristics must be cultivated and developed to support expatriate cross-cultural success. These characteristics can be cultivated through relying on multiple relationships, such as mentors, coaches, host country liaisons, expatriate colleagues, friends and family as well as by supporting and mentoring others. These characteristics can also be developed through specific cultural experiences, knowledge and skill building resources, as well as developing an informed view of self and identity clarity through reflective activities. Originality/value Based on the overall findings, a cross-cultural professional success model was designed and implications for scholarship, organizational effectiveness and cross-cultural leadership practice are presented.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".