Automated evaluation of website navigability: an empirical validation of multilevel quality models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Websites are an important and efficient means of communication for companies wishing to interact with their clients. Therefore, research has focused on evaluating how websites should be structured to ensure their quality. The majority of this research has focused on evaluating the quality of individual pages or that of a site as a whole. In this article, we propose the use of two‐level models that combine evaluations at the page level with evaluations at the site level, and applied them to the problem of evaluating the navigability of websites. To test our models, we conducted a study with 21 subjects who had to complete navigation tasks on several websites, and compared their quality judgments to those produced by single and two‐level quality models. We found that two‐level models are better predictors of navigability. Finally, we show how two‐level models are able to suggest modifications to improve site navigability. Copyright © 2012 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it