Constructing Careers ‘in chaos’. Exploring Career Progress of Expatriates and Repatriates on Career Ladder or Career Lattice Pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores expatriate and repatriate career progress on career ladder and lattice paths through the theoretical lenses of Career Construction Theory, Chaos Theory of Careers and the Career Ladder versus Career Lattice model. The research method is an exploratory, qualitative study, using purposive sampling and snowball sampling techniques. 78 interviews were conducted with expatriates and repatriates originating from countries across the globe. Findings reveal that respondents had multidirectional global careers, shifting from linear, hierarchical career ladder paths to non-linear, unpredictable career lattice paths along which they made career progress.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it