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Record W2910124992 · doi:10.11575/prism/35726

Post-secondary students with disabilities share stories of belonging

2019· dissertation· en· W2910124992 on OpenAlexaboutno aff
Patricia Solesbee Foy

Bibliographic record

VenueOpen MIND · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyPedagogySociology

Abstract

fetched live from OpenAlex

This study examined a sense of belonging as integral to inclusion for students with disabilities within a post-secondary context. Grounded in a critical disability lens, coupled with identity theories, a narrative research approach was used. Nine students with disabilities from a small, Canadian, rural college shared stories of belonging and of the significance they ascribe to belonging in their overall post-secondary experience. Three prominent themes, narratives of becoming a student, narratives of engagement and narratives of barriers to belonging were uncovered. Narratives of becoming a student relate to the development of a student identity and its reciprocal relationship to the development of a sense of belonging. Narratives of engagement capture the positive and/or negative interactions of students with faculty and peers and the impact on belongingness. Narratives of barriers to belonging highlight the environmental, physical, systemic and attitudinal obstacles encountered by students. Analyzing narrative accounts through critical disability and identity frameworks revealed in-depth understandings of students’ belonging experiences. Results of this study offer both theoretical and practical implications for institutions to consider in their commitment to cultivating belonging-centred campuses. Further, I suggest including disability as part of institutional diversity and also the use of a multifaceted critical lens of identity and disability in which to view stories of belonging.

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 categoriesInsufficient 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 score0.098
Threshold uncertainty score0.997

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.000

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 head0.046
GPT teacher head0.404
Teacher spread0.358 · 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
Published2019
Admission routes1
Has abstractyes

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