In The Eye Of The Hurricane: Sustainable Careers Under Lockdown
Bibliographic record
Abstract
The sustainable careers framework proposes that careers are sustainable if they are characterized by three main indicators: health, happiness, and productivity, when responsibility for these career outcomes is shared between individuals and organizations. The COVID-19 pandemic has created a major career disruption for many individuals due to layoffs, reduced work hours and increased work-life conflict. Using a mixed methods design, we explore how individuals perceive their careers, and specifically indicators of sustainability, during the first lockdown in the UK. In the qualitative Study 1 we identify themes that characterize the common experiences during this early stage of the pandemic, namely employer support, careful optimism and strengthened relationships. Then, in the quantitative Study 2, we empirically test a research model that links the concept of employer support with indicators of sustainable careers. We investigate the mediating role of career empowerment, which is a motivational cognitive construct that captures individual cognitions of agentic control over one’s career. In addition, we investigate the moderating role of agreeableness in the relationship between the individual and the organization. Our research provides a rich snapshot that depicts perceptions of careers in crisis, which has both theoretical and practical implications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".