Careers and Career Success Across National Contexts
Bibliographic record
Abstract
Research on ‘global careers’ to date has been dominated by work on the career attitudes and behaviors of expatriates, and other globally mobile professionals. Whilst this represents an important body of work, our understanding of careers in the international context remains piecemeal given its relatively narrow focus, limited consideration of the perspective of multiple stakeholders (other groups of employees, organizations), and lack of attention to context. This symposium includes a collection of six papers that represent a much broader spectrum of research on careers across national contexts. In addition to the mainstream research on global careers, we feature research from two emerging, complementary research streams: comparative careers, and organizational career management across countries. The Meaning of Career Success across Occupations: Empirical Evidence from German-speaking Europe Presenter: Petra Eggenhofer-Rehart; WU Vienna Presenter: Martin Gubler; Schwyz U. of Teacher Education Young, Blue and Happy? Career Success and Life Satisfaction among Young Blue-Collar Workers Presenter: Jos Akkermans; Vrije U. Amsterdam Presenter: Wolfgang Mayrhofer; WU Vienna Presenter: Dominik Zellhofer; WU Vienna The Impact of Organizational Career Management on Objective Career Success in Context Presenter: Silvia Bagdadli; Bocconi U. / SDA Bocconi Presenter: Martina Gianecchini; U. of Padova Presenter: Robert Kase; U. of Ljubljana Presenter: Jon P. Briscoe; Northern Illinois U. Presenter: Katharina Chudzikowski; U. of Bath Presenter: Astrid Reichel; WU Vienna U. of Economics and Business Employability as a Function of Age and OCM Practices: A Cross-country Comparison Presenter: Silvia Dello Russo; ISCTE-IUL, Instituto U. de Lisboa Presenter: Emma Parry; Cranfield U. Presenter: Janine Bosak; Dublin City U. Presenter: Sonia Ferencikova; School of Management Bratislava Presenter: Michael Dickmann; Cranfield U. Does International Experience Pay Off? Impact on Employability and Career Success Presenter: Maike Andresen; U. of Bamberg Presenter: Florian Buschhardt; U. of Bamberg Presenter: Richard Cotton; U. of Victoria Presenter: Mila Borislavova Lazarova; Simon Fraser U. Presenter: Eleni Apospori; Athens U. of Economics and Business Subsidiary Managers and Code-switching Presenter: Soo Min Toh; U. of Toronto Presenter: Hugh P. Gunz; U. of Toronto
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".