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Record W3176701651 · doi:10.1111/anti.12752

Spaces of Social Recomposition: Resisting Meaningful Work in Social Cooperatives in Italy

2021· article· en· W3176701651 on OpenAlexaff
Valentina Castellini

Bibliographic record

VenueAntipode · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAusterityOverworkSociologyWork (physics)Social movementSocial workReproductionPublic relationsInequalityPolitical economyPolitical scienceEconomic growthLabour economicsEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Workers’ experiences in Italian social cooperatives expose the tension between the goal of desirable change that these social economy organisations pursue and the demanding working conditions shouldered by staff. Born from the radical movements of the 1970s, social cooperatives deliver care and community services that seek to counter inequalities and marginalisation. This important work fuses employment with activism. Yet, cooperatives often rely on casualised labour practices that normalise overwork. A noble mission does not guarantee cooperatives will also be sustainable work environments. Based on extensive qualitative research conducted in Milan, this paper explores how, since 2013, a collective of social economy workers has been mobilising to challenge poor working conditions, query the ways workers participate in them, and connect labour demands with broader struggles against austerity and impoverishment. These organising efforts pursue social recomposition : a form of labour struggle that exceeds the workplace and embraces the sphere of social reproduction. Combining a workerist framework of class composition with feminist insights, this paper invites attention to the ways workers inhabit and struggle within, against and beyond their work.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.346
Teacher spread0.304 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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