Co-designing a Technology Probe with Experienced Designers
Bibliographic record
Abstract
Technology probes are low-fidelity devices that can be used to understand research participant’s lived experiences, but they are not usually subject to iterative design. There are opportunities in human-computer interaction to develop technology probes through co-design, by including diverse perspectives during probe development. To explore this opportunity, five design researchers with different disciplinary and cultural backgrounds engaged with a technology probe to support daily reflections, discussed new directions in a co-design workshop and developed narratives to negotiate possibilities of the probe. This paper presents observations described by each of the researchers using the probe, and insights from the process we followed. We discuss how the the designers’ postitionalities are reflected in the processes, and how they brought value by shaping narratives of the different roles a technology probe might take. We also discuss how we may use co-design of technology probes as a generative method, highlight the importance of open-endedness in the process, and reflect on lessons learned.
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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 | 0.059 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".