Cross-Level Dynamics between Changing Organizations and Career Patterns of Reduced-Load Professionals
Bibliographic record
Abstract
Integrating research on careers, flexible work arrangements, and open systems views of organizational change, we investigate how evolution in the broader organizational context interacts with professional career trajectories over time. Interviews were conducted six years apart (1997 and 2003) with 17 major employers in North America and 36 managers and professionals in those firms who were working on a reduced-load basis by choice in 1997. Overall, we found that career patterns are impacted by the dynamic combination of individual-level and contextual factors. Specifically, while changes in core business/client base, internal structure changes, and industry turbulence were associated with higher proportions of returns to full-time work, financial threat was associated with lower levels of return to full-time work. We identified four cross-level dynamics (co-optation, synergy, decoupling, and tug of war) that capture different patterns of interaction between individual work arrangement trajectories and larger trends occurring at the organizational or industry level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".