Careers in context: An international study of career goals as mesostructure between societies' career‐related human potential and proactive career behaviour
Bibliographic record
Abstract
Abstract Careers exist in a societal context that offers both constraints and opportunities for career actors. Whereas most studies focus on proximal individual and/or organisational‐level variables, we provide insights into how career goals and behaviours are understood and embedded in the more distal societal context. More specifically, we operationalise societal context using the career‐related human potential composite and aim to understand if and why career goals and behaviours vary between countries. Drawing on a model of career structuration and using multilevel mediation modelling, we draw on a survey of 17,986 employees from 27 countries, covering nine of GLOBE's 10 cultural clusters, and national statistical data to examine the relationship between societal context (macrostructure building the career‐opportunity structure) and actors' career goals (career mesostructure) and career behaviour (actions). We show that societal context in terms of societies' career‐related human potential composite is negatively associated with the importance given to financial achievements as a specific career mesostructure in a society that is positively related to individuals' proactive career behaviour. Our career mesostructure fully mediates the relationship between societal context and individuals' proactive career behaviour. In this way, we expand career theory's scope beyond occupation‐ and organisation‐related factors.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".