Crip languaging: A linguistic and rhetorical analysis
Bibliographic record
Abstract
This dissertation is made up of three articles which examine the language disabled people use for themselves through the lenses of linguistics, metaphor, and rhetoric to find the unique language tools we use. The first article utilizes embodied rhetoric to examine the social media response on Twitter to Sia’s film Music through study of the #ActuallyAutistic hashtag in 2020 and 2021. This study shows how autistic people use métis on Twitter to push back against the anti-autistic rhetoric of Sia’s film. The project is significant because it goes beyond studies of access to examine what happens when disabled people are given space and agency and demonstrates the importance and power of social protest and autistic métis rhetoric which change what it means to be rhetorical. The second article is an examination of the metaphors women use to talk about living with chronic conditions, and how these metaphors contrast with extant metaphor research, as well as the metaphors we culturally use to talk about chronic conditions and disability. This study found that participants used the metaphor “CHRONIC CONDITIONS ARE A CYCLE,” a heretofore unresearched conceptual metaphor, particularly regarding disability and health. This research has implications for how health care providers talk to patients and use metaphor, especially for the growing number of chronically ill people with long-COVID in the U.S. and around the world. The final article uses Conversation Analysis (CA) and Crip Linguistics to examine how women with chronic illnesses identify with/out disability. I find that many only use this terminology when they are in need of accommodation at work or school, which may be related to the stigma associated with “disability” and the societal expectation of normality and abledness. As a whole, this dissertation research demonstrates the unique features and rhetorics of disability, neurodivergence, and chronic illness. The three papers together demonstrate ways that disabled people crip language and “alter and reinvent the world to make access happen” through language (Henner and Robinson, 2023), using tools like conversational preference, rhetoric, metaphor, and social media activism, which can change how society sees and treats disabled people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".