Crip Material Exploration as an Assistive Technology Research Framework: Situating Interdependence in Empowered Disabled Making
Bibliographic record
Abstract
Human-Computer Interaction (HCI) scholars have grappled with the question of how to directly involve people with disabilities (PwDs) in assistive technology (AT) research. While there is recognition that PwDs possess unique expertise, this understanding often remains limited to an assumption that the bounds of this expertise end with the embodied knowledge related to their impairments. However, PwDs possess expertise that extends beyond this narrow definition. In navigating different contexts within their communities of care, PwDs build empowered expertise through interdependence. This knowledge, grounded in everyday material experiences, can significantly inform future AT design practices. I offer a new crip-material exploration (CME) framework to expand the understanding of PwD expertise. In doing so, future AT research can better encompass the full range of the social connections and material experimentation that enriches the lived experiences of PwDs. The connective, inter-group mediation that PwDs are skilled in throughout everyday interactions points toward the importance of creating new research approaches to engaging with that expertise. CME can be leveraged by HCI scholarship to understand how the empowered interdependence of PwDs strengthens current and future AT design. A workshop structure is proposed to help guide scholars in implementing CME into future research designs.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".