Highly Oppositional Occupations and Cognitive Behavioral Script-Based Mechanisms of Work–Home Conflict
Bibliographic record
Abstract
We introduce novel theory to explain how and why some forms of work are particularly likely to foster work–home conflict. While all occupations have entrenched and prescriptively normative cognitive behavioral scripts that guide effective work task execution, we argue that some occupations’ scripts are predominantly oppositional in nature, and largely inappropriate outside of the work domain. We articulate four forms of oppositional thoughts and behaviors (physical, interpersonally hostile, authoritarian, and intellectual) that are learned and reinforced at work. We explain how performing these scripts can desensitize workers to enacting oppositional behaviors, and how these scripts can be inadvertently cued and enacted at home. We introduce moderators to consider for whom this process might be more or less likely. Our theory enhances understanding of how work can conflict with home through cognitive behavioral processes, beyond known temporal and affective mechanisms. We also contribute to boundary theory perspectives on work–home conflict to theorize a novel detractor to effective work–home role segmentation, beyond boundary flexibility and permeability: role cue similarity across work and home. We conclude with a discussion of how our theory can generate research across work–home conflict, boundary theory, and occupations literatures, as well as 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".