Bibliographic record
Abstract
We base this paper on the assumption that design research and practice carry gaps that tends to slow down their mutual enrichment. To reconcile both, we share our ongoing research project that explores how activity theory can contribute to design research. First, we present an adaptation of the activity theory model for design activities. In particular, we developed the model and a related template to support students' learning experiences as part of their workshop projects. Several teams of design students were solicited to test and participate in the various phases of our research project. They were invited to monitor their activities and collect data while conducting their design project and using the designerly activity theory template. This paper aims to synthetize and discuss past research initiatives that were conducted over the years. We aim to demonstrate the potential and relevance of activity theory to support, structure and enrich research-through-design. We conclude the paper by closing the gap between design research and practice for the value of reflection-on-action and learning opportunities in design education.
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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 | 0.062 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".