Prosumer Capitalism: The Meanings and Motivations for Working in the Platform Economy
Bibliographic record
Abstract
People can earn income using digital platforms in many ways. Some earn income by sharing their homes on Airbnb. Others engage in ridesharing services using Uber, or become delivery couriers for DoorDash and Amazon Flex. Some even monetize their time spent online by completing paid surveys or transcribing audio/video clips. But is working in the growing platform economy just a side hustle to earn extra money? Or does it indicate a more permanent trajectory of work that is not standardized, salaried, and fulltime? Given that one-in-five Canadians work in the platform economy, it is necessary to uncover why people pursue platform work over conventional employment (and vice versa). Even more, because this sector is not entirely governed by current regulations, platform workers comprise a vulnerable group. Thus, unearthing the potential impacts, benefits, challenges, and consequences for working in the platform economy is important.\nThis dissertation investigated individuals’ motivations for pursuing platform work and how participants rationalized their experiences of work within prosumer capitalism. This exploratory and interpretivist research is informed by the lived experiences of 71 participants performing 23 types of platform work to offer a more holistic view of this sector. The results were analyzed using social action theory and conspicuous prosumption.\nThe participants discussed a range of motivations, challenges, working patterns, and perspectives on platform work. This study argues that participants are rational social actors who make decisions to work shaped by conspicuous prosumption: the spectacle of excess spending and gratuitous working. Their decisions to pursue platform work are guided by their goals for consumption and meaningful production. More legal protections are needed to protect vulnerable platform workers who experience wage theft and dangerous working conditions. Moreover, participants work more hours than the average Canadian by taking on multiple jobs. Most take on added work as a personal choice but some are forced to do so out of financial necessity. Some reported a pessimistic outlook regarding the future of work in Canada. While participants enjoyed the autonomy, flexibility, and benefits of multiple jobholding in the context of the platform economy; they valued stable, fulltime, and salaried conventional employment.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".