User-centred authentication feature framework
Bibliographic record
Abstract
Purpose – This paper aims to propose that more useful novel schemes could develop from a more principled examination and application of promising authentication features. Text passwords persist despite several decades of evidence of their security and usability challenges. It seems extremely unlikely that a single scheme will globally replace text passwords, suggesting that a diverse ecosystem of multiple authentication schemes designed for specific environments is needed. Authentication scheme research has thus far proceeded in an unstructured manner. Design/methodology/approach – This paper presents the User-Centred Authentication Feature Framework, a conceptual framework that classifies the various features that knowledge-based authentication schemes may support. This framework can used by researchers when designing, comparing and innovating authentication schemes, as well as administrators and users, who can use the framework to identify desirable features in schemes available for selection. Findings – This paper illustrates how the framework can be used by demonstrating its applicability to several authentication schemes, and by briefly discussing the development and user testing of two framework-inspired schemes: Persuasive Text Passwords and Cued Gaze-Points. Originality/value – This framework is intended to support the increasingly diverse ecosystem of authentication schemes by providing authentication researchers, professionals and users with the increased ability to design, develop and select authentication schemes better suited for particular applications, environments and contexts.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".