What Happens When the Gig is Up? Gig Work Experiences and Future Career Prospects
Bibliographic record
Abstract
Despite the increasing popularity of gig work, relatively little is known about how such work experiences may influence future career trajectories. We explore employment signals associated with gig work histories to gain some understanding of the mechanisms accounting for these signals. We carry out two survey experiments that simulate the hiring decisions of jobseekers with differing gig work histories to examine the labor market signals associated with gig work and the extent to which gig work experiences are (more) less preferred to standard employment histories. The findings of study 1 reveal that for lower skilled jobs (i.e., the delivery driver) gig work experience is not perceived as inferior, nor does it impede employment prospects in the traditional labor market. However, for jobs requiring higher levels of education and/or skill (i.e. the retail manager and the graphic designer), a candidate with standard employment plus a gig work side-hustle is perceived qualitatively the same, but those with only gig work experience, even that which makes use of similar skillsets, are perceived as less desirable. Furthermore, we find that perceptions of lower human capital and a negative signal mediate the relationship between gig work experiences and the decision to hire the applicant.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".