A multi-method approach for user-centered design of identity management systems
Bibliographic record
Abstract
Identity management (IdM) comprises the processes and infrastructure for the creation, maintenance, and use of digital identities. This includes designating who has access to resources, who grants that access, and how accountability and compliance is maintained. IdM has become an important aspect of IT security infrastructure in organizations, and some consider it to be the most important solution for enabling compliance. To facilitate identity management, usable technological solutions are important. In this ongoing research, we plan to study the practice of identity management from a socio-technical point of view, and study how technology can improve IdM. Our final goal is to develop recommendations for user-centered design of IdM systems. We've devised a multi-method approach to address this problem. To begin with, we performed a case study of IdM adoption and use in an insurance organization. The case study provides us with a high level understanding about the problem domain and directions for the rest of our research. We plan to continue our research in two phases: (1) evaluate the usability of an IdM system using heuristic evaluation, and (2) perform a field study to further our understanding about IdM practices and technologies, validate the results of our heuristic evaluation, and develop recommendations for user-centered design of IdM systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".