Empowering spaces: Customizable furniture solutions for Toronto's mobile communities
Bibliographic record
Abstract
The paper tries to investigate the influence of parametric design tools and digital fabrication to create customizable furniture solutions for Toronto’s mobile communities like International Students and Non-resident working class. The recent investigations done in the field of parametric design tools focuses on things like how it makes the designer creative or creating products that are never ideated alongside the end user and their precise needs. The problem is that modularity, multiple purposes and customizability is being brought into the market without bringing the end user to the designing table. The methods used in this paper are based on human centred design approaches of co-design with the end user to create prototypes which can then be analysed by user testing to gather feedback and create iterations to better computational or generative algorithms. This study found evidence that algorithms created for customization of furniture by involving the end user at every step of the stage creates solutions that have intuitive use cases which the intent for was never there. What emerged are customizations that can help produce preferences for the end user at hand to provide agency in their daily activities. The research provided the base of what kind of customizations can be desired by the end user and how a rigorous study can be conducted after this exploratory research to better optimize the algorithms and also the user interface of how the customizations can be done by the end user.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".