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Record W4285125074 · doi:10.46743/2160-3715/2022.5078

Finding Resilience Through Research: Completing a Ph.D. While Parenting an Intellectually Disabled Adult “Child”

2022· article· en· W4285125074 on OpenAlexafffund
L. K. Hutton

Bibliographic record

VenueThe Qualitative Report · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsQueen's University
FundersQueen's University
KeywordsAutoethnographyPsychologyInsiderNarrativeContext (archaeology)Psychological resilienceInclusion (mineral)Disability studiesPedagogyNarrative inquiryEquity (law)Abandonment (legal)SociologyDevelopmental psychologySocial psychologyGender studiesEpistemologyPolitical science

Abstract

fetched live from OpenAlex

Unlike the progression of most traditional-aged, college or university students, my non-traditional, academic trajectory as a parent-caregiver to an intellectually disabled (ID) adult has been fraught with barriers, disruption, and discouragement. Motivation to complete my doctorate rests on a commitment to disability issues, caregiver activism, and intellectual capacity-building of my self. Guided by the “evocative” autoethnographic methodology of Bochner and Ellis (2016), this “insider’s” narrative retrospective autoethnography will attempt to shed light on and evoke an understanding of a doctoral student caregiver’s context and experience in the academy. It encompasses embodiment, a geographically constrained sense of place, marginalization, and neoliberal abandonment—elements that have contributed to my sense of burden, inferiority, and non-competitiveness in the academy. An analysis of my autobiographical experience would suggest that increased institutional awareness of a caregiving student’s complex obligations, recognition of their non-traditional contributions to society, and offerings of flexible modes of participation could improve equity and inclusion for caregivers who are challenged in extraordinary ways.

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)0.026
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.491
GPT teacher head0.583
Teacher spread0.092 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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