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Record W1531610931 · doi:10.18061/dsq.v33i3.3209

Historical perspectives on studying families of children with disabilities: A case for critical research

2013· article· en· W1531610931 on OpenAlexaff
Priya Lalvani, Lauren Polvere

Bibliographic record

VenueDisability Studies Quarterly · 2013
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsDouglas Mental Health University Institute
Fundersnot available
KeywordsSociocultural evolutionDisability studiesNarrativeMedicalizationSocial model of disabilityMeaning (existential)Narrative inquiryQualitative researchGrounded theorySociologyHegemonyPsychologyDevelopmental psychologyGender studiesSocial sciencePsychotherapistPsychiatryPolitical scienceAnthropologyLinguisticsPolitics

Abstract

fetched live from OpenAlex

Historically, research on families of children with disabilities has been framed by the medical model, which views disability as a biological deficit, necessarily associated with burden and grief. This body of research does not acknowledge the sociocultural contexts in which the meaning of disability is embedded, or the manner in which deficit-based hegemonic discourses shape the familial experience of having a child with a disability. In this paper, we problematize the medicalization of the phenomenon of disability and resulting research inquiries. We propose the use of narrative inquiry, theoretically grounded in sociocultural perspectives, as a critical research methodology for situating the lives of these families in cultural and sociopolitical contexts. Narrative inquiries complicate conclusions drawn from the medical model and contribute to new insights on the ways in which interpretations of having a child with a disability are mutually negotiated between individuals and society. Keywords: families; children with disabilities; narrative inquiry; disability studies; critical disability theory; qualitative methodology

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)0.029
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0430.096
Scholarly communication0.0140.020
Open science0.0030.012
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.167
GPT teacher head0.453
Teacher spread0.285 · 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 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

Citations42
Published2013
Admission routes1
Has abstractyes

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