Enhancing Career Success: The Role of My Parents’ Occupational Footsteps in My Career.
Bibliographic record
Abstract
Employees started to overtake the responsibility for their career and proactively self-manage their own career, because the accountability shifted from the organization to the individual’s responsibility. This shift has led to a growing interest in career self-management research. Beside various self-management techniques, like for example career adaptability, perceived employability, or job crafting, occupational following (OF), that is following in one’s parents’ profession, might be an interesting aspect to consider. In ancient times, it was custom that the son or daughter follows into his/her parents’ occupational footstep. Nowadays, OF applies to up to one quarter of employees. The present paper seeks to explore if OF actually enhances career success and could therefore be a fruitful self-management characteristic. In doing so, we apply theories of social identity and social capital as well as signaling theory, to argue why OF might boost one’s objective and subjective career success. Our hypotheses are subsequently tested with a structural equation model on a weighted sample of 3,384 individuals from the Swiss Household Panel. The findings show that OF is positively related to subjective career success but not to objective career success. The implications for theory and praxis are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".