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Record W2737221060 · doi:10.25071/2291-3637.40227

The Sharing Economy as Primitive Accumulation: Locating the Political-Economic Position of the Capital-Extractive Sharing Economy

2017· article· en· W2737221060 on OpenAlexaffvenue
Chris Fairweather

Bibliographic record

VenueHPS The Journal of History and Political Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsCarleton University
Fundersnot available
KeywordsCapitalismContext (archaeology)Order (exchange)Marxist philosophyPosition (finance)DeregulationCapital (architecture)EconomicsPoliticsEconomyPolitical economyMarket economyEconomic systemPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.275
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes2
Has abstractyes

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