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Record W4307562821 · doi:10.1111/glob.12407

Unpaid labour and territorial extraction in digital value networks

2022· article· en· W4307562821 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Networks · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsYork University
Fundersnot available
KeywordsValue (mathematics)Flexibility (engineering)Division of labourWork (physics)EconomicsBusinessIndustrial organizationLabour economicsMarket economyEngineeringComputer scienceManagement

Abstract

fetched live from OpenAlex

Abstract Production in knowledge and data‐intensive industries is powered by work that can, in theory, be done from anywhere, via cloudwork platforms. Cloudwork platforms govern data value chains in distinct ways to concentrate power and extract value at the global scale. We argue that unpaid labour is a systemic mechanism of accumulation in these digital value networks. In this paper we demonstrate how it is tied to platform business models and facilitated by elements of platform governance including monopsony power, a high degree of spatial flexibility in sourcing labour, regulatory unaccountability and digital enclosure. We draw on a survey of 699 workers on 14 platforms in 74 countries to show that unpaid labour is an engine of South–North value extraction, and workers in the global South perform more unpaid labour than counterparts in the global North. Our findings have important ramifications our understanding of the changing international division of labour and platform capitalism.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.006
GPT teacher head0.244
Teacher spread0.238 · 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