Exploring Conceptualizations of Disability Using Story-Completion Methods
Bibliographic record
Abstract
This study explored conceptualizations of disability pertaining to peer relationships versus romantic relationships, as well as type of physical disability, using story-completion methods. Seventy-four graduate and undergraduate students from a Canadian university completed one of two versions of a story stem featuring an individual with a physical disability who was either a classmate or a potential romantic partner. Through the process of thematic analysis, three themes were generated as patterns across stories: (1) assumptions about disability present from first glance; (2) uncertainty in navigating negative assumptions of disability; and (3) from discomfort to acceptance of disability through social connection. Storylines differed depending on the type of relationship (i.e., peer or romantic) in both story length and outcome of the relationship. Findings suggest the usefulness of the relatively infrequently used method of story completion for assessing students’ narratives and discussion of meanings surrounding differing relationships with persons with a disability. This study further develops our understanding of cultural norms of disability, as well as highlights the importance of disability knowledge and interaction between persons with and without a disability, to foster positive change in representations and perceptions of disability.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".