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Record W4378573967 · doi:10.1111/ntwe.12273

Platform cooperatives and the dilemmas of platform worker‐member participation

2023· article· en· W4378573967 on OpenAlexaff
Morshed Mannan, Simon Pek

Bibliographic record

VenueNew Technology Work and Employment · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsUniversity of Victoria
FundersEuropean Commission
KeywordsDynamics (music)MultihomingBusinessWork (physics)FacilitationScale (ratio)Investment (military)Open platformKnowledge managementPublic relationsManagementEconomicsEngineeringPolitical scienceComputer scienceSociologyThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Despite the surge of interest in platform cooperatives, we have a limited understanding of the dynamics of platform worker‐member participation in these cooperatives. Drawing on interviews with 21 senior leaders and founders of platform worker cooperatives, we investigate the dynamics of platform worker‐member participation, finding that these cooperatives experience some successes and many challenges. We then build theory about how four distinct features of platform worker cooperatives—the facilitation of multihoming, the physically untethered nature of work, the relatively high importance of scale as a strategic imperative, and the relatively low importance of initial platform worker‐member investment—influence these participation dynamics. We find that the platform and worker cooperative organisational models are in tension with one another when brought together within a platform worker cooperative, leading to positive and negative effects on participation.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.018
Scholarly communication0.0090.008
Open science0.0020.011
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.043
GPT teacher head0.256
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations35
Published2023
Admission routes1
Has abstractyes

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