Bibliographic record
Abstract
The design of technology occurs in a rich, nuanced and complex rhetorical space. Technical teams engage in negotiations, and at times argue, about design. We claim that user experience (UX) practice, at its heart, is a rhetorical endeavor, and this aspect of UX practice has been underexplored. To bridge the gap between UX theory and practice, we pose the research question: What strategies and tactics do UX practitioners use to convince or persuade others about design? To answer this question, we interviewed experienced UX practitioners and present the results of these interviews as a taxonomy of rhetorical strategies situated by an awareness of rhetorical complexity and the impact of context. The results of the study demonstrate that normative UX methods and practices discussed in the literature are chosen, adapted or dismissed as savvy rhetors flex their metis.
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 | 0.038 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".