Self-initiated expatriation: a career perspective through a social chronology lens
Bibliographic record
Abstract
Purpose This is a conceptual paper, intended to link the constructs self-initiated expatriation (SIE) and career. The author suggests that regarding SIE as an episode in a career allows one to use ideas from the careers literature to suggest novel areas for research on SIE, thereby contributing to the SIE literature. The author employs a particular perspective on career – the social chronology framework (SCF) – to show how the framework can suggest these novel areas of research on self-initiated expatriation. The SCF views careers through three perspectives related to the space within which the career takes place, the career actor who “has” the career, and the time over which the career plays out. By looking at SIEs through each of these perspectives in turn a number of research questions are suggested that have the potential to enrich the SIE literature. Design/methodology/approach The paper first considers the construct of career and shows how self-initiated expatriation fits with it. Next, it introduces the SCF, and finally shows how it can be used to derive ideas for research on self-initiated expatriation. Findings There are none, given that this is a conceptual paper. Research limitations/implications The paper suggests future directions for research on SIEs. Originality/value The author believes that the application of the SCF to the study of self-initiated expatriation is novel.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".