Inclusive, Accessible, Sustainable – Designing A Different Future
Bibliographic record
Abstract
Merging concepts of accessible, sustainable, and inclusive design practices with respect to the fashion industry, this thesis examines ways of doing design that encapsulates many design methodologies into one. I argue that the overall aim of this thesis is to prove that there are ways that designers can be mindful of accessibility, inclusion, and the environment from conception to production to consumption as a whole, while honoring their aesthetic viewpoints. Through a literature review, case study, practice-based reflection, and methodology, a look into what the future of design might look like is explored. The literature review examines perspectives from the design gap illustrated in the introduction, which discusses why there is not more crossover among the following categories in meaningful ways: accessibility and inclusion, contemporary aesthetics, environmental responsibility. The case study provides insights from within the design world, presenting the results from four surveys and three interviews with practicing Canadian designers/design businesses. The practice-based reflection portrays the experiences of the author as this thesis work was progressing and as they assembled their own practice into a tangible design studio, serving clients and conceptualizing inherently adaptive products. The final section showcases the iterative and qualitative design methodology that has been developed from the combination of conducted research. This thesis is intended as an offering of curated research findings and lived experience perspectives, that can be built on for further analysis, critique, and development.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".