Careers and labor-market stability vs. dynamisms: Using big-data to optimize career trajectories for better outcomes
Why this work is in the frame
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Bibliographic record
Abstract
In career and human resource management, long-standing questions about career dynamics, and more specifically, how to optimize career progress via dynamic moves or stable employment, remain unresolved. Challenging the myth of career stability in the modern labor market, this study leverages a unique, nation-wide big data set of approximately 3 million Bulgarian workers and 300,000 employers over an 11-year period to definitively answer the long-standing debate about career dynamism. We address conflicting arguments about the existence of substantial contemporary career dynamics. Theoretically, we expand both the boundaryless career and career ecosystem theories, subsequently providing new evidence for key scholarly debates regarding new careers' dynamics and practical advice for individuals. We employed linear probability analysis and sensitivity analysis to test our hypotheses. Our findings reveal a highly fluid environment where less than a third of the workforce experiences career stability. We identify eight distinct clusters of career boundary-crossings (job, employer, and sector changes) and demonstrate that, contrary to traditional views, frequent career moves are often associated with better financial outcomes. Notably, job and employer changes yield significant short-term wage growth and long-term wage increases, while sector changes often lag behind. We also uncover crucial temporal dynamics: the positive wage impact of career transitions amplifies over time, whereas the boost to wage growth is most pronounced immediately after a move. The implications for individual career management, organizational talent strategies, and national labor policies in navigating this dynamic landscape are substantial. • We answer an ongoing debate within the career field about boundaryless vs. bounded careers. • Using Big Data of 3,000,000 individuals & 300,000 employers, comprising one country's entire working population • Addressing conflicting arguments about the existence of substantial contemporary career dynamics • Expanding both the boundaryless career and career ecosystem theories • Finding a dynamic labor market characterized by career boundary-crossings: job, employer & sector
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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