How have careers changed? An investigation of changing career patterns across four generations
Bibliographic record
Abstract
Purpose – Popular literature argues that successive generations are experiencing more job changes and changes of employer. The “new careers” literature also proposes that career mobility patterns are becoming more diverse as people engage in more downward and lateral job changes and changes of occupation. The purpose of this paper is to test these assertions by comparing the career mobility patterns across four generations of workers. Design/methodology/approach – The authors analyzed the career mobility patterns of four generations of Canadian professionals ( n =2,555): Matures (born prior to 1946); Baby Boomers (1946-1964); Generation Xers (1965-1979) and Millennials (1980 or later). Job mobility, organizational mobility and the direction of job moves were compared across groups through analysis of variance. Findings – Significant differences were observed in job mobility and organizational mobility of the various generations, with younger generations being more mobile. However, despite significant environmental shifts, the diversity of career patterns has not undergone a significant shift from generation to generation. Originality/value – This is the first quantitative study to examine shifting career mobility patterns across all four generations in today’s workplace. The authors extend previous research on generational differences in job mobility by using novel measures of career mobility that are more precise than extant measures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".