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Record W4407081374 · doi:10.1080/14616742.2024.2447594

Pluralizing social reproduction approaches

2025· article· en· W4407081374 on OpenAlexaff
Alessandra Mezzadri, Sara Stevano, Donatella Alessandrini, Hannah Bargawi, Juanita Elias, Shireen Hassim, Surbhi Kesar, Jayanthi Thiyaga Lingham, Serena Natile, N. Neetha, Lyn Ossome, Parvati Raghuram, Dzodzi Tsikata, Stefanie Wöhl

Bibliographic record

VenueInternational Feminist Journal of Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsCarleton University
Fundersnot available
KeywordsReproductionSociologyBiologyEcology

Abstract

fetched live from OpenAlex

The concept of social reproduction (SR) has gained renewed interest in the past decade. Discussed and elaborated by generations of feminists, the concept offers a rejection of productivism and the possibility of (re)telling the history of capitalism and its contemporary dynamics through the work and practices of “life making.” Yet, it is undeniable that much of the “old” and “new” theorizing around SR comes predominantly from the Global North. Hence, we argue, a pluralizing of SR approaches (SRAs) is needed. This article draws on existing debates on SR to pluralize their theoretical premises, disciplinary boundaries, and empirical reach to suggest a global progressive agenda centered on SRAs that is able to speak to the challenges and complexities of processes of life making worldwide. Foregrounding such complexity, the article considers conceptual issues, argues that location is central to pluralizing SRAs, and discusses the associated methodological and political questions. We conclude that pluralizing SRAs through paying attention to location is the first step toward identifying the multiple and heterogeneous ways in which processes of SR are structured and operate under capitalism and toward developing solidarities able to challenge the oppressions of global capitalism and reclaim life-making activities and spaces.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.251

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 head0.102
GPT teacher head0.374
Teacher spread0.272 · 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 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

Citations20
Published2025
Admission routes1
Has abstractyes

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