Bibliographic record
Abstract
Purpose To explore self‐directed expatriates' relationships with their home and host countries by drawing on an existing model of expatriate managers' allegiance to home and host organizations. Design/methodology/approach Using a qualitative methodology and thematic analysis, the study draws on interviews with 30 expatriate academics in four countries. Specifically, the paper draws on Black and Gregersen's model of allegiance to home and host organizations to explore the different dimensions and the strength and weakness of those relationships. Findings The findings suggest that, while the model of allegiance presents a useful starting‐point, further modifications are required in order to cater for the complexity and dynamism of relationships with home and host countries. Research limitations/implications Whereas the paper focuses on UK expatriates, it may be that other nationals may experience different relationships with their home and host countries. Moreover, it may be useful to explore the relationships of other self‐directed expatriates such as managers and corporate executives, or medical personnel. Originality/value The specific value of the paper is that it explores a hitherto under‐researched theme and provides an insight into the identified dimensions of self‐directed expatriates' relationships with their home and host countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".