Work-Life Synchronicity: An Ethnographic Study of Workers Who Live in Vans
Bibliographic record
Abstract
After 50 years of studying how work and life interact, research continues to consider each of these domains as relatively rigid or static, with some seeing the two in conflict with the other (Greenhaus & Beutell, 1985) and others seen each enriching the other (Greenhaus & Powell, 2006). However, both work and life may not be static but instead be fluid and changing (Greenhaus & Kossek, 2014; Mark & Su, 2010; Spreitzer, Cameron, & Garrett, 2017), each with the potential to be adapted to better align with the other domain. To investigate the notion of the alignment of work and life as fluid phenomenon, we studied workers who live in vans, people maintain mobility in both work and non-work domains. In this ethnographic study, the principle investigator lived in a van while meeting and interviewing 18 workers who live in vans. Through thematic analysis, a variety of themes related to the extent to which work and home are aligned or in conflict were identified, including (1) the identification of the van as home, (2) sense of identity, (3) career and financial freedom and (4) work-life synchronicity. We consider how these findings contribute to theory on work-life synchronicity for all workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".