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Record W3120290054

'Disabled or not, people just want to feel welcome': stories of microaggressions and microaffimations from college students with intellectual disability

2020· article· en· W3120290054 on OpenAlexvenueno aff
Laura T. Eisenman

Bibliographic record

VenueCritical education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsAbleismStorytellingNarrativePsychologyIdentity (music)Intellectual disabilityDisability studiesNarrative inquiryInterpersonal communicationSocial psychologyMedia studiesPedagogyGender studiesSociologyAestheticsLiterature
DOInot available

Abstract

fetched live from OpenAlex

As part of a larger storytelling project with college students belonging to minoritized social groups, nine young adults from an inclusive college program for students with intellectual disability (ID) participated in narrative interviews. All were invited to tell stories about campus incidents of microaggression and microaffirmation related to their disability. They also were invited to tell stories about other social identities they claimed. Stories were analyzed thematically and for correspondence with findings from previous studies involving other social identity groups. Students told a variety of stories about interpersonal incidents on campus that made them feel respected or disrespected. They also shared stories of institutional encounters that influenced their sense of acceptance at the university. Although they told more stories about microaffirmations, they were not immune to microaggressions. However, many of the students' microaffirmation stories placed importance on not being perceived as different rather than a clear affirmation of disability identity. Students' stories have implications for fostering a campus climate where students with ID are respected and included and where ableism is addressed in substantial 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)0.000
metaresearch head score (Gemma)0.006
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 score0.095
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.419
Teacher spread0.347 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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