Making Biomaterials for Sustainable Tangible Interfaces
Bibliographic record
Abstract
In this studio, we will explore sustainable tangible interfaces by making a range of biomaterials that are bio-based and readily biodegradable. Building off of previous TEI studios that were centered around one specific biomaterial (i.e., bioplastics at TEI’22 and microbial cellulose at TEI’23), this studio will provide participants the ability to experience a wide variety of biomaterials from algae-based bioplastics, to food-waste-based bioclays, to gelatin-based biofoams. We will teach participants how to identify types of biomaterials that are applicable to their own research and how to make them. Through hands-on activities, we will demonstrate how to implement biomaterials in the design of sustainable tangible interfaces and discuss topics sensitized by biological media such as more-than-human temporalities, bioethics, care, and unmaking. Ultimately, our goal is to facilitate a space in which HCI researchers and designers can collaborate, create, and discuss the opportunities and challenges of working with sustainable biomaterials.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".