Unmaking@CHI: Concretizing the Material and Epistemological Practices of Unmaking in HCI
Bibliographic record
Abstract
Design is conventionally considered to be about making and creating new things. But what about the converse of that process – unmaking that which already exists? Researchers and designers have recently started to explore the concept of “unmaking” to actively think about important design issues like reuse, repair, and unintended socio-ecological impacts. They have also observed the importance of unmaking as a ubiquitous process in the world, and its relation to making in an ongoing dialectic that continually recreates our material and technological realms. Despite the increasing attention to unmaking, it remains largely under-investigated and under-theorized in HCI. The objectives of this workshop are therefore to (a) bring together a community of researchers and practitioners who are interested in exploring or showcasing the affordances of unmaking, (b) articulate the material and epistemological scopes of unmaking within HCI, and (c) reflect on frameworks, research approaches, and technical infrastructure for unmaking in HCI that can support its wider application in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.030 | 0.042 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 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".