The Conceptualization, Measurement, And Influence of a Millennial Career Mindset
Bibliographic record
Abstract
This study extends research on generational differences beyond stereotypes and demographic differences to the development of a mindset towards careers. Because Millennials grew up in a labor market with less job stability, more contingent work, and a rapidly changing global marketplace, we theorize the development of a multidimensional millennial career mindset (MCM) that encompasses the underlying implicit theories and beliefs that emerge from the millennial context. Specifically, we focus on four dimensions of the MCM: job-hopping norm, self-directed career development, social media embracement, and perceived employability. While all participants in the modern workforce are shaped by the context, we expect the MCM will be stronger for Millennials than non-Millennials. We also hypothesize that the relationship between job satisfaction and withdrawal intentions will be weaker for those individuals possessing a stronger MCM. We collected data from 115 employees at a Canadian organization and found that a MCM moderated the relationships between job satisfaction and organizational commitment and turnover intentions. The findings suggest that job satisfaction is less pertinent to influencing the stay intentions of employees with a strong MCM.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".