Solitude in Remote Work: Navigating the Challenges of Agency and Self-Management
Bibliographic record
Abstract
Employees returned to in-person work globally with ease of the COVID-19 pandemic, yet working remotely for at least a portion of one’s workdays (i.e., hybrid work) has remained prevalent and popular. Employees are therefore choosing to continue working alone in solitude. Scholars have often assumed that solitude leads to loneliness, but this sole focus on the negative side of solitude does not allow for the possibility that many employees embrace and enjoy solitude in remote work. In this research, through a qualitative study that analyzes 801 employees’ naturalistic reports of solitude from Reddit, we explore employees’ experiences of solitude in remote work from a more balanced perspective. We find that employees’ experiences of solitude depend on how well they internalize and mentally represent their immediate, physical environment, as well as their distant, socially embedded community. Such representations and preservations then depend on the implicit relationship theories about the relationships between their selves and these two environments that they form and carry with them from in-person work. These theories influence their experiences of solitude, at the beginning, and their coping with such experiences afterward.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 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".