Do “one-size” employment policies fit all young workers? Heterogeneity in work attribute preferences among the Millennial generation
Bibliographic record
Abstract
There has been a stream of research that explores how the present generation of workers (i.e., Millennials) may be different from previous generations (e.g., Baby Boomers and Gen Xers). This line of research often considers Millennials as homogeneous and concludes any differences to be “generational effects.” However, it is unlikely for a generation, which spans almost 20 years, to be uniformly homogeneous with respect to their work values and attitudes. Findings on generational differences conducted in the United States are also often generalized to other countries, ignoring the potential for national influences. In this regard, we apply a multi-method approach using three samples to demonstrate that there are differences within the Millennial generation that affect work values, preferences for work/life balance, and attraction to employer attributes. Specifically, we focus on the heterogeneity resulting from differences in age, gender, relationship status, and nationality. Our results suggest that Millennials are not as homogeneous as we assumed, and this can limit the effectiveness of managerial policies designed to improve individual and work outcomes for an entire generation of workers. Our study demonstrates that it is important for us to understand how individual, relational, and contextual factors may contribute to the heterogeneity within a generation. JEL CLASSIFICATION M12, M14, M54
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".