Interaction design in the university : designing disciplinary interactions
Bibliographic record
Abstract
Interaction design (ID) as a field emerged in the late 1990s with roots in both the HCI and design communities. We ask whether the ‘interdisciplinary’ agenda of the 3rd paradigm of HCI can be accommodated in the traditional disciplined university. An alternate model of ‘interdisciplinarity’ offers one way forward, but calls for clarity on the question of what interaction design aspires to be. We offer the notion of 'disciplined transdisciplinarity' as an exciting and perhaps necessary way of solving the complex problems that ID researchers face, and illustrate this with examples drawn from the area of emotional design and assessment. Our bridge between 3rd paradigm, knowledge production and what we are calling 'disciplined transdisciplinary' yields insights into the path toward institutionalizing and legitimating research on ID and academic careers in this field in the university.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".