It's Cold Outside: Measuring the Challenges of Independent (Gig) Work
Bibliographic record
Abstract
Gig workers face a number of challenges that differ in nature or intensity from those experienced by traditional organizational workers. We developed and tested a measure of six challenges commonly faced by independent workers—viability, organizational, identity, relational, emotional, and career-path uncertainty—in three samples of platform-based workers, including Mechanical Turk workers and highly skilled independent scientists. Initial evidence for the measure’s validity included its convergent and discriminant validity in relation to measures of depletion, thriving, resilience, and loneliness. We confirmed the measure’s factor structure, showed its relationship with thriving and perceived meaningfulness of work, and examined differences across different subgroups in the sample of independent scientists. Our findings help us to better understand the types of stressors independent workers face in the gig economy, and our diverse set of samples provides evidence of the universality of these stressors. Further, our developed measure represents an important research tool for future studies of independent (gig) workers’ experiences.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".