Usability : gaining a competitive edge : IFIP 17th World Computer Congress - TC13 stream on usability: gaining a competitive edge, August 25-30, 2002, Montréal, Québec, Canada
Bibliographic record
Abstract
Preface. Committee Chairs. International Programme Committee. Part One: Keynote Speaker.Deconstructing Silos: The Business Value of Usability in the 21stCentury G. Lindgaard. Part Two: Technical Sessions. User-Centred Design Process. A Method-Independent Process Model of User-Centred Design T. Jokela. Use and Reuse of HCI Knowledge in the Software Development Lifecycle: Existing Approaches and What Developers Think E. Metzker, H. Reiterer. Analyzing the Role of Organizational Culture in the Implementation of User-Centered Design: Disentangling the Approaches for Cultural Analysis N. Iivari. Usability of Mobile Systems. The Importance of User Roles in Feature Bundling Decisions in Wireless Handheld Devices: Strategic User Needs Analysis (SUNA) S. Narasimhan, G. Lindgaard. Exploring the Use of the Mobile Phone N. Ozkan. Usability Engineering Milestones In Complex Product Development - Experiences At Nokia Mobile Phones: Industrial Experience P. Ketola. User Requirements Analysis. Use Case Maps: A Roadmap for Usability and Software Integrated Specification A. Alsumait, A.Seffah, T. Radhakrishnan. User Requirements Analysis: A Review of Supporting Methods M. Maguire, N. Bevan. Usability Evaluation. Specifying and Evaluating Usability Requirements Using the Common Industry Format: Four Case Studies N. Bevan, et al. Formal Usability Testing of Interactive Educational Software: A Case Study D. Van Greunen, J. Wesson. Tools. EQUAL: Towards an Inclusive Design Approach to Novice Programming Languages and Computing Environments for Native Users B. Patil, et al. MouseLupe: An Accessibility Tool for People with Low Vision L. Silva, et al. A Framework for Rapid Mid-Fidelity Prototyping of Web Sites D. Engelberg, A. Seffa. Web Usability. Keep on Trying: Online Securities Trading Sites D. Morenkov. User Satisfaction, Aesthetics and Usability: Beyond Reductionism G. Lindgaard, C. Dudek. Frameworks for Usability. Evaluating Security Tools towards Usable Security: A Usability Taxonomy for the Evaluation of Security Tools Based on a Categorization of User Errors J. Kaiser, M. Reichenbach. Improving Usability in Decision Support Systems: Practical Use of the Decision Enquiry Approach for Requirements Analysis C. Parker. Teaching Human-Computer Interaction: Qualitative Support for an Alternative Approach. P. Kotze, L. Oestreicher. Usability: Who Cares? Usability: Who Cares? An Analysis of Indifference Towards. Usability Within the IT Industry T. McCoy. Author Index. Keywords Index.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.112 | 0.043 |
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".