Thinking rhizomatically and becoming successful with disabled students in the accommodations assemblage: Using storytelling as method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".