Crafting a place for attending to the things of design at CHI
Bibliographic record
Abstract
Over the past two years, we have organized workshops at the CHI conference that have focused on the “Things of Design Research. The goal of these workshops is simple: to explore and develop a venue at CHI for research through design (RtD) practitioners to materially share their work with each other. RtD often centers on the making of things— artifacts, systems, services, or other knowledge in the interaction-design and human-computer interaction (CHI) research communities. Yet, over the years, we have felt that the things of design research have remained conspicuously overlooked, under-engaged with, and, for the most part, absent from the CHI conference. If RtD is to continue to develop as a research practice in the HCI community—and we want to build a community of designers doing research with and through designed objects—we need more things at CHI.
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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 | 0.035 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.072 | 0.040 |
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".