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Record W4200234070 · doi:10.1177/08861099211056038

Using a Resistance Lens to Understand Performative Compliance of Low-Income Lone Mothers

2021· article· en· W4200234070 on OpenAlexafffund
Silvia Vilches, Jane Pulkingham

Bibliographic record

VenueAffilia · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsResistance (ecology)Agency (philosophy)SociologyWelfare reformSocial psychologyWelfarePsychologyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

The agency of lone mothers who rely on government income supports is often erased by the discourse of dependency, especially under welfare-to-work eligibility criteria. Here we apply the concept of small acts of micro-resistance in constrained circumstances, augmented by conceptualization of resistance as conscious oppositionality and intentionality to understand the agency of lone-mothers who receive income-assistance (IA) as they make-do and raise children under state- and market-enforced rules. Using a resistance lens reveals the interconnected importance of everyday acts like “talking back” to income-support staff, surreptitious gleaning of goods for resale, and re-storying the self. We describe these in three modalities: resistance as evasion and subterfuge; resistance through asserting positive identities; and resistance in forging their own path. Using a conceptual framework of resistance reveals the extent to which women’s survival and capacity to raise children are contingent on a performance of compliance, demonstrating the impacts of welfare-to-work on female-headed lone parent families.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.139
GPT teacher head0.363
Teacher spread0.223 · 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 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

Citations3
Published2021
Admission routes2
Has abstractyes

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