Bibliographic record
Abstract
When our company chose to adopt an Agile development process for new products, our User Experience Team took the opportunity to adjust, and consequently improve, our user-centered design (UCD) practices. Our interface design work required data from contextual investigations to guide rapid iterations of prototypes, validated by formative usability testing. This meant that we needed to find a way to conduct usability tests, interviews, and contextual inquiry—both in the lab and the field—within an Agile framework. To achieve this, we adjusted the timing and granularity of these investigations, and the way that we reported our usability findings. This paper describes our main adaptations. We have found that the new Agile UCD methods produce better-designed products than the “waterfall” versions of the same techniques. Agile communication modes have allowed us to narrow the gap between uncovering usability issues and acting on those issues by incorporating changes into the product. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a
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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 | 0.153 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".