Drawing Out Alternative Methods for Understanding the Material Culture of Disability
Bibliographic record
Abstract
This presentation illustrates how drawings can be used as methods during data collection and data analysis to better understand the material culture of disability. Material culture is the study of how people interact with spatial environments and things. Even though material culture focuses on ‘thingness’ it typically involves collecting and analyzing data in relatively traditional ways such as conducting interviews and doing observations, yet studying thingness necessitates alternative ways of creating knowledge because less tangible concepts such as human movement, memories, and identities are significant to material culture studies. As such, this presentation demonstrates how drawing can aid towards better understanding pertinent concepts in material culture through studies that engage with disability through architectural design in Belgium, the Netherlands and Canada. The aim of our work is to better understand spatiality and thingness through the sensorial bodies of researchers and participants with different abilities. Drawings created as data and techniques used in data analysis through studies at museums, care homes and private homes are highlighted. These methods are re/articulations and re/presentations of embodied ways of experiencing and knowing designed spatiality and the material culture of disability; however, we believe that these methods of doing research have the potential to go beyond the study of thingness to aid in explorations into other queries.
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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 | 0.089 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".