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Record W4386890331 · doi:10.1177/20438206231202820

Social reproduction, infrastructure, and the everyday

2023· article· en· W4386890331 on OpenAlexaff
Gökbörü Sarp Tanyildiz

Bibliographic record

VenueDialogues in Human Geography · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsBrock University
Fundersnot available
KeywordsReproductionSociologyTransformative learningSocial reproductionUrbanizationContext (archaeology)Object (grammar)EpistemologyPoliticsEnvironmental ethicsEconomic geographySocial scienceEcologyGeographyPolitical scienceComputer scienceBiology

Abstract

fetched live from OpenAlex

One of the main preoccupations of contemporary (feminist) urban theory is to conceptualize social reproduction, infrastructures, and everyday as the constitutive elements of the processes of urbanization. In this commentary, I engage with McFadden's contribution to these efforts from the standpoint of the study of concept formation. This commentary dwells on McFadden's theoretical object of knowledge (i.e. ‘infrastructures of social reproduction') within the empirical context of ‘educational landscapes’ both in terms of the method of its construction and the political consequences of this method. I argue that while it is important to insist on the inseparability of social reproduction and infrastructures within the spatiotemporal unfolding of urbanization, our theoretical attempts must go beyond asserting this inseparability to be able to produce transformative social knowledge of the (non-)urban. A way in this direction, I suggest, is the method of recuperation of the specificities that produce social reproduction and infrastructures as both indivisible and individual under concrete socio-spatial histories, conditions, and principles.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.074
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.294
Teacher spread0.270 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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