Relational Infrastructures and Gig Worker Well-Being: Social and Parasocial Interaction Rituals
Bibliographic record
Abstract
In organizations, reliably patterned interactions and role-relationships offer emotional resources central to workers’ well-being. In the gig economy, however, workers lack a ready relational infrastructure. Through two studies, a longitudinal repeated measures study of independent scientists and an interview study of gig and organizational workers paired in a variety of occupations, we examine how work relationships shape workers’ well-being in the gig economy and develop theory about how gig workers build relational infrastructure. Analysis of the longitudinal data reveals how relational challenges affect gig workers’ well-being. Analysis of the rich interview data shows that while organizational workers can rely on stable relationships, gig workers intentionally craft interactions with a variety of relational partners, including imagined interactions, to support their well-being. Mobilizing theory of social interaction and rituals, we demonstrate how these interactions, which we characterize as reaching out or reaching in, create positive energy and emotions that form the building blocks of gig workers’ relational infrastructure, ultimately helping workers cope with relational challenges. We detail contributions to scholarship on work relationships and well-being, interaction rituals, and people’s experiences in the gig economy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".