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Record W4390987129 · doi:10.1177/16094069241227075

Toward Access Justice in the Academy: Centring Episodic Disability to Revision Research Methodologies

2024· article· en· W4390987129 on OpenAlexafffund
Lacey Croft, Elisabeth Harrison, Josh Grant-Young, Kelly McGillivray, Jennifer C. H. Sebring, Carla Rice

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of ManitobaWestern UniversityUniversity of GuelphYork University
FundersSocial Sciences and Humanities Research Council
KeywordsContext (archaeology)StorytellingDigital storytellingEconomic JusticeNarrativePrivilege (computing)Narrative inquirySociologyPsychologyPublic relationsPedagogyPolitical science

Abstract

fetched live from OpenAlex

This article explores questions of disability access justice within the academy through the lens of an online digital/multimedia storytelling (DS) research workshop conducted during the COVID-19 pandemic. Our investigation uncovers how the shift from in-person to online DS methodologies created newfound opportunities for participation, particularly for individuals with episodic disabilities (EDs). Through an analysis of three co-author participants’ multimedia/digital stories and their reflective insights, we investigate the interplay between research methodologies and the broader context of disability access within the academy. Participants’ stories of inventive adaptations unfold against a backdrop of experiences in traditional academic settings that privilege normative ways of working and seldom accommodate diverse rhythms and access needs, including of students, faculty and staff. In light of these narratives, we advocate for an ongoing commitment to access-centered practices in research and work beyond crisis situations. The article concludes that academic research enterprises can accommodate a wider spectrum of participants—particularly those with episodic disabilities—and enhance research outcomes by recognizing and anticipating diverse bodyminds within the design of research methodologies and techniques.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.122
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.706
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1220.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.924
GPT teacher head0.789
Teacher spread0.135 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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
Published2024
Admission routes2
Has abstractyes

Explore more

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