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Record W4385289842 · doi:10.1177/0308518x231187402

Bringing life's work to market: Frontiers, framings, and frictions in marketised social reproduction

2023· article· en· W4385289842 on OpenAlexaff
Emily Rosenman, Jessa Loomis, Dan Cohen, Tom Baker

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsQueen's University
FundersMarsden Fund
KeywordsReproductionSocial reproductionRealmNonmarket forcesSociologyEconomicsSocial capitalPolitical scienceMarket economyEcologyFactor marketSocial scienceBiologyLaw

Abstract

fetched live from OpenAlex

The introduction to this theme issue discusses a series of papers examining the increasing marketisation of social reproduction and its effects on systems that sustain human and social life. This is done by examining the frontiers, framings, and frictions that arise when market systems are constructed to enable capital accumulation in the realm of social reproduction. Frontiers identify the expansion of market logic into new areas, framings explore how financial actors attempt to bring the logic of social reproduction within the purview of market competition, and frictions highlight the various tensions that generate resistance to the roll out of market logics. Through establishing these three areas, we argue that both market structures and systems of social reproduction should be understood as geographically variegated and, at times, uncertain. This variegation necessitates an understanding of marketised social reproduction as forged through complex articulations of market and non-market logics. Using cases from surrogacy to smart electricity meters, the papers in this theme issue illustrate that while these articulations may generate benefits for some individuals, households and communities, such processes of marketisation can introduce new layers of inequity and undermine the ethical relations and social commitments that sustain life—in the service of enabling accumulation.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.190
Teacher spread0.174 · 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.

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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

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