A Theory of Aberrant Work-Life Navigation
Bibliographic record
Abstract
Everyone experiences major life transitions (e.g. relocation, job loss, birth of a child), and increasingly so as economic, technological, and social environments become more turbulent. Yet in accounting for how work-life decision-making associated with these transitions occurs, we argue that extant theory overfocuses on individual agency and rational thinking. In this article, we bridge an epistemological divide between the study of major life transitions and work-life decision-making by advancing a narrative theory of aberrant work-life navigation. Our theory overcomes blind spots around the study of “real life,” lived experiences, introducing work-life navigation as a messy, complex, and volatile process, capturing the ontology of how individuals experience major life transitions. We point out factors that inhibit rationality and constrain agency traditionally ascribed to work-life decision-making at the individual (intuitive and unconscious thoughts, emotions, impulsivity, and inaction) and contextual (work-life stakeholders, cultural norms, and regulations) levels. Further, we apply our theorizing to the most studied outcomes associated with major life transitions—work-life balance, conflict, and enrichment—to highlight how these are inherently subjective and, at times, determined by factors entirely beyond one’s control. We conclude by offering a future research agenda to empirically test our theory of aberrant work-life navigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".