A multidisciplinary approach to improving the user experience
Bibliographic record
Abstract
In this paper we discuss how the combined efforts of three teams, Information Development (ID), Test, and User Experience Design (UED), improved the overall customer experience with store development in IBM® WebSphere® Commerce, a software package that enables merchants to host their businesses online.The project began in the spring of 2001 with a formal effort to solicit customer feedback on documentation. A few months later, a test team was created to simulate the customer experience. The test team performed the store development tasks as customers would, following only the instructions in the documentation. As ID and Test continued working together, they began to realize that, in order to improve the documentation, they had to revisit and improve the actual store development. This led to the involvement of the UED team.UED, Test, and ID continued working together to understand the store development processes our customers follow and the challenges they face. Through collaboration, the team identified areas for improvement in both the tools and the documentation, and developed an action plan to address these problems.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".