Sustainable Unmaking: Designing for Biodegradation, Decay, and Disassembly
Bibliographic record
Abstract
Unmaking is a counterpart to making and creating new things that has emerged as a concept of interest in diverse parts of the HCI community. Unmaking has been posed as an ally to sustainability, encouraging designers to foreground issues relating to reuse, repair, obsolescence, degradation, and decay early in their design process. As a follow-up to the 2022 Unmaking@CHI workshop, this workshop will bring together researchers and practitioners interested in unmaking as it relates to sustainability and will focus primarily on exploring the role of unmaking in material practices, drawing upon the growing body of unmaking theory to explore future research opportunities for designing physical things with sustainable materials that are transient, degradable, and intentionally unmake-able. In addition to considering the pragmatics of what and how to unmake, we seek to articulate the relationships among unmaking and other related emerging themes and sustainable material practices – including biodegradation, designing with more-than-human agencies, reuse, and repair – and propose guidelines for designing for the unmaking of physical artifacts that are sustainable, equitable, and respectful of all entities involved.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".