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Record W3118334905 · doi:10.15353/cjds.v9i3.647

Uncertain Subjects: Shaping Disabled Women’s Lives Through Income Support Policy

2020· article· en· W3118334905 on OpenAlexaffvenue
Sally A. Kimpson

Bibliographic record

VenueCanadian Journal of Disability Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGovernment (linguistics)CitizenshipEmbodied cognitionIndependence (probability theory)Power (physics)Income SupportReading (process)SociologyGender studiesPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article provides a critical reading of one aspect of the “third mobilization of transinstitutionalization” (Haley & Jones, 2018), focused on how power is exercised through the B.C. government income support program (or the ambiguously-named B.C. Benefits), shaping the embodied lives of women living with chronic physical and mental impairments. I research and write as a woman living with a disabling chronic illness whose explicit focus is power: how it is enacted and what it produces in the everyday lives of women with disabling chronic conditions living on income support. I too have been the recipient of disability income support. Thus, my accounts are ‘interested.’ My writing seeks to create a disruptive reading that destabilizes common-sense notions about disabled women securing provincial income support benefits, in particular in British Columbia (B.C.), interviewed as part of my doctoral research. Despite public claims by the B.C. government to foster the independence, community participation, and citizenship of disabled people in B.C., the intersection of government policy and practices and how they are read and taken up by disabled women discipline them in ways that produce profound uncertainty in their lives, such that these women become uncertain subjects (Kimpson, 2015).

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.001
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 head0.131
GPT teacher head0.390
Teacher spread0.259 · 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.

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
Published2020
Admission routes2
Has abstractyes

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