The Impact of Gig Workers’ Satisfaction on Social Mobility
Bibliographic record
Abstract
A growing number of individuals are transitioning from traditional full-time occupations to new forms of employment, consisting of short and flexible contracts, often relying on digital platforms such as Uber, DoorDash, and Freelancer (Wu & Huang, 2024). This type of employment, recognized as gig working, offers flexibility, autonomy and various direct and indirect opportunities provided by the gig economy (Singha & Saikia, 2024). However, gig workers' satisfaction with the gig economy is not a straightforward relationship. Some gig workers, generally or partially, show satisfaction with their jobs across various industries, including delivery, information technology, administrative work, sales, and service. Their satisfaction level may vary depending on income, skill development, having leisure time, or work-life balance. In particular, some show dissatisfaction with their work in the gig economy and prefer to switch their work from one sector to another, or they consider returning to traditional employment. Gig workers’ satisfaction with their work is a multi-faceted phenomenon explained by several factors, such as platform-related, company-specific, demographic and psychological elements (Jabagi et al., 2019; Tiwari et al., 2024). The literature shows mixed results regarding the effect of these variables on gig workers’ satisfaction. Additionally, we are less informed about the consequences of satisfaction or dissatisfaction in the gig economy. Understanding the interplay between the gig workers’ job satisfaction and the longer-term consequences of this type of work is crucial. Identifying the implications of gig workers’ (dis)satisfaction can practically and theoretically contribute to the long-term well-being of the workers and the performance of the gig platforms. For instance, dissatisfaction can cause a sequence of behaviors leading to job changes (Gazioglu & Tansel, 2006). Moreover, it can trigger retraining and skill development for facilitating upward social mobility. This is the main contribution of this proposal. We draw from social stratification theory (Grusky, 2019) to investigate the complex nature of gig work and how episodes of satisfaction or dissatisfaction influence individuals' efforts toward upward social mobility. First, we categorize different types of gig work and their associated satisfaction levels. Next, we theorize the relationship between (dis)satisfaction and mobility efforts such as job search or acquiring new skills. We empirically test our framework using data from the UK understanding society survey (2023). This research can offer valuable insights for academia and industry seeking to understand better and support the evolving dynamics of the gig economy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".