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Record W3212473253 · doi:10.32920/ryerson.14660937.v1

The inner self-narratives and academic self-perceptions of those with learning disabilities in post-secondary settings

2021· preprint· en· W3212473253 on OpenAlexaboutno aff
Sofia Alexandra Mendes Bronze

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipNarrativeIdentity (music)PerceptionLearning disabilityPsychologyDisability studiesSelf-conceptSelf-advocacyNarrative inquiryLearning disabledPedagogyDevelopmental psychologySocial psychologySociologyGender studiesAesthetics

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the various disabled identities of those with learning disabilities in higher educational settings, and its impact on academic self-worth. The majority of scholarship has essentialized both disabled identity and academic self-perception, fostering the victimization of those with learning disabilities in the pursuit of their education. This study problematized the medical model, viewing disability as an internal and fixed identity, negatively implicating self-worth. In contrast, this study incorporated a critical disability theory, to highlight the social construction of disability, complimented with a postmodernist lens to appreciate the fluidity of identity and perceptions. A narrative methodological approach was utilized to give voice to the experiences and stories of five self-identifying learning disabled students from Ryerson University. The findings of this research suggest that learning disabled student relate to three different types of disability narratives or identities, implicating their academic worth in many 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 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)0.004
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.010
Scholarly communication0.0070.004
Open science0.0010.008
Research integrity0.0010.002
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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same topicDisability Education and EmploymentFrench-language works237,207