Individuals' career perceptions in different institutionalized contexts: A comparative study of career actors in liberal, coordinated, hierarchical and mediterranean market economies
Bibliographic record
Abstract
Abstract Leveraging Weiner's attribution theory of intrapersonal motivation at the micro level and varieties of capitalism theory at the macro level, we conduct a multi‐country and cross‐level study examining whether individuals' career goals (i.e., perceived importance of learning and development), behaviors (i.e., proactive career behaviors), and outcomes (i.e., perceived employability) as well as the relationships between these variables, differ between different market economies. We challenge extant literature that focuses on the agentic role of individuals and understates the role of context (i.e., market economy influence) in an individual's career development. Using multilevel structural equation modeling, we draw on a survey of 15,201 individuals between 2014 and 2016 from 22 countries representing four different varieties of capitalism. The results showed that workers in hierarchical (HME) and Mediterranean (MME) market economies systematically differed from individuals in coordinated (CME) and liberal (LME) market economies in proactive career behaviors and perceived employability. Moreover, while the positive relationship between perceived importance of learning and development and proactive career behaviors was stronger in CMEs and LMEs compared to HMEs and MMEs, the positive association between proactive career behaviors and perceived employability was weaker. Our study bridges the micro‐macro gap in career studies, adding new insights into the ongoing conversation of contextual influence in individuals' career development.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".