A tale of two generations: a time-lag study of career expectations
Bibliographic record
Abstract
Purpose As young individuals transition from educational settings to embark on their career paths, their expectations for their future careers become of paramount importance. Ng et al. (2010) examined the expectations of young people in post-secondary education in 2007; those colloquially referred to as “Millennials” or “GenY”. The present study replicates Ng et al .'s (2010) study among a sample of post-secondary students in 2019 (referred to as Generation Z or GenZ) and compares the expectations of young adults in GenY and GenZ. Design/methodology/approach This study employs a time-lag comparison of GenY and GenZ young career entrants based on data collected in 2007 ( n = 23,413) and 2019 ( n = 16,146). Findings Today's youth seem to have realistic expectations for their first jobs and the analyses suggest that young people continue to seek positive, healthy work environments which make room for work–life balance. Further, young people today are prioritizing job security and are not necessarily mobile due to preference, restlessness or disloyalty, but rather leave employers that are not meeting their current needs or expectations. Practical implications Understanding the career expectations of young people allows educators, employers and policymakers to provide vocational guidance that aligns those expectations with the realities of the labor market and the contemporary career context. Originality/value While GenY was characterized as optimistic with great expectations, GenZ can be described as cautious and pragmatic. The results suggest a shift away from opportunity, towards security, stability, an employer that reflects one's values and a job that is satisfying in the present.
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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 | 0.003 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".