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Record W3110904763 · doi:10.22215/etd/2020-14134

Exclusionary Inclusion?: The Realities of Academic Accommodation at University for Students with Invisible Physical Chronic Conditions

2020· dissertation· en· W3110904763 on OpenAlexaff
Lindsay Johnstone

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsCarleton University
Fundersnot available
KeywordsAccommodationInclusion (mineral)Perspective (graphical)Identity (music)Academic communityQualitative researchPedagogyPsychologySymbolic interactionismQualitative propertySociologyMathematics educationSocial psychologySocial science

Abstract

fetched live from OpenAlex

The following thesis is a case study that discusses the various impacts of academic accommodations for undergraduate students who self-identify as having invisible physical chronic conditions (IPCCs).This research draws on qualitative data from interviews with undergraduate students and academic accommodation centre staff.Using a symbolic interactionist perspective, I explore why students who identify as having IPCCs either use, or do not seek out, academic accommodations.I investigate how students' identities are impacted, arguing that there is negative impact on identity for some students, particularly vis-a-vis how they are viewed within the University community.The research further uncovers the current academic accommodations system's challenges, and opportunities for positive change in terms of improving the access and experience of academic accommodations for students.The research discusses the concept of the "continuous assessment" model as an alternate design and practice of academic accommodation that arguably would benefit all post-secondary students.

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)0.009
metaresearch head score (Gemma)0.020
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 score0.027
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0270.021
Scholarly communication0.0140.010
Open science0.0030.035
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.040
GPT teacher head0.407
Teacher spread0.367 · 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
Published2020
Admission routes1
Has abstractyes

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Same topicDisability Education and EmploymentFrench-language works237,207