Self‐initiated expatriation and self‐initiated expatriates
Bibliographic record
Abstract
Purpose This paper aims to move towards clarification of the self‐initiated expatriate/expatriation construct with the aim of extending and deepening theory development in the field. Design/methodology/approach Drawing on Suddaby's think piece on construct clarity, this paper applies his proposed four elements; definitional clarity, scope conditions, relationships between constructs and coherence, in order to clarify the SIE construct. Findings The discussion examines the “problem of definition” and its impact on SIE scholarship. The spatial, temporal and value‐laden constraints that must be considered by SIE scholars are expounded, and the links between SIE research and career theory are developed. From this, potential research agendas are proposed. Research limitations/implications This is a conceptual piece which, rather than giving precise research data, encourages further thinking in the field. Originality/value Although the definitional difficulties of SIEs have been identified in previous literature, this is the first attempt to clarify the boundaries of SIE and its interconnectedness with other related constructs.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".