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Record W3211366198 · doi:10.1111/nin.12475

Thinking rhizomatically and becoming successful with disabled students in the accommodations assemblage: Using storytelling as method

2021· article· en· W3211366198 on OpenAlexaff
Iris Epstein, Jarrett Robert Rose, Linda Juergensen, Roxanne Mykitiuk, Katie MacEntee, Lindsay Stephens

Bibliographic record

VenueNursing Inquiry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsStorytellingEquity (law)Diversity (politics)SociologyInclusion (mineral)PedagogyPsychologyEngineering ethicsPolitical scienceSocial scienceNarrativeEngineering

Abstract

fetched live from OpenAlex

The number of disabled students enrolled in higher education institutions is increasing. Yet in disciplines such as nursing, where placements are an important part of student success, students' lived experiences, though an important and necessary aspect of promoting equity, diversity, and inclusion, has been ignored. In this paper, we respond to such issues by creating and utilizing a novel storytelling method that harnesses the antiessentialist philosophy of Deleuze and Guattari. Storytelling empowers students to both describe their experiences and inform institutions on how to better serve them, and we use concepts from Deleuze and Guattari to provide a framework for thinking about students and their pathways toward success as multiple. As we show, applying storytelling as a method through this lens offers an expansion of strategies to put students first and, therefore, promote equity at the administrative, research, educational, and practical levels. We describe how thinking rhizomatically opens new avenues of insight, allowing for the creation of institutional assemblages based on a diverse array of students' needs, enabling them to become successful in their own 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.073
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.119
GPT teacher head0.469
Teacher spread0.350 · 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.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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