Transformation volatility and the gateway model for Web page migration to small screen devices
Bibliographic record
Abstract
More people are using their smaller devices to access the Web. In this paper, we concentrate on the effect of migrating Web pages from large screened devices to small screened devices for users who use a Web site first on the larger screen and then use the same site on the small screen. We examine transformation volatility, including cognitive and navigational factors, related to the user experience while switching between devices for Web page use. A user's cognitive volatility can be minimized by using a transformation method that both enables the user to reuse their existing mental model of a Web page first viewed on the large screen on the small screen and by decreasing the cognitive load required to comprehend the interface components. The navigational volatility relates to a user's expectation of how to navigate a new instance of a page on a different screen size and is influenced by changes to layout, content, location of options, and legibility. We propose a model for automatic transformation of Web pages called the gateway that creates reduced replica of the source page. The Gateway transformation model minimizes the effect of transformation volatility for users switching between different screen sizes. Based on a subjective ranking of twenty-five randomly generated Gateways from original Web pages, twenty-two of the Gateway pages were ranked excellent or good. We examine the transformation volatility with three transformation models: direct, linear, and the Gateway.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".