Psychological contracts: enhancing understanding of the expatriation experience
Bibliographic record
Abstract
In this introduction, we undertake a critical review of the state of research examining psychological contracts (PCs) as they pertain to the experience of expatriation and expatriates. Though expatriation as an activity has diversified greatly in recent decades – with the growth of self-initiated expatriation, more short-term, flexible and commuter assignments and a broadening of the expatriate profile with a wider range of people choosing to expatriate – there has been limited research published thus far examining expatriates’ perceptions of their PC and how it is affected by working inter-culturally. This introduction briefly traces the history of PC research and expatriates/expatriation and the employment relationship, and then considers the extant research specifically examining PCs in relation to expatriates/expatriation. The articles included in the special issue address a range of areas identified in the call for papers and provide valuable insights into expatriation and expatriates’ experience including: expatriate PC breach; expatriate PC fulfilment; pre- and post-assignment PC as perceived by repatriates; PC of flexpatriates; and, episodic formation of expatriate PCs. Given the relatively underexplored area of expatriate/expatriation-related PC research, the special issue establishes a platform for undertaking future research in this field.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".