The Sharing Economy as Primitive Accumulation: Locating the Political-Economic Position of the Capital-Extractive Sharing Economy
Bibliographic record
Abstract
There has been much debate in the columns of newspapers as to how we should understand the sharing economy, but as yet, much of the debate is largely superficial, garnering little attention in terms of rigorous academic analysis. In this paper, I argue that the rise of the capital-extractive sharing economy model employed by companies like Uber and Airbnb cannot be understood outside of the political-economic context from which it emerges. Drawing on the work of Marxist scholars like David Harvey, I analyze such models through the lens of primitive accumulation, positioning their development as positive evidence of Harvey’s theory that capitalism seeks to colonize new spheres of social life in order to offload the tensions of its own internal conflicts; in this case, labour market insecurity. Further, I argue that the rise of the capital-extractive sharing economy should be recognized as constituting a further entrenchment of the global neoliberal project, particularly as it stands to affect union organizing, force deregulation in favour of free market fundamentals, and further deepen the labour market insecurity from which it rises in the first place.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".