Bibliographic record
Abstract
As web sites continue to grow in their complexity, one of the most important usability design decisions is how to structure the web site topic hierarchy. This decision lays the groundwork for designing other aspects of the site, e.g., the home page table of contents, and for categorizing new documents in the topic structure over the life of the site. Organizing web sites is a timely topic as evidenced by the recent spate of publications on this topic [1] [2] [3] and by NIST's recent release of a tool, WebCAT, to help users participate in organizing their web site. More significantly, the organization is probably the limiting factor on success for web sites that provide useful information.If anything is clear about the organization of web sites, it is that there is no single best way to go about it. Different types of web sites seem to demand different approaches to organization. For example, a site geared toward product support might use a task-oriented scheme that steps users through a process of problem-solving. Other sites, such as an online shopping site, might be organized according to categories of products, in order to allow efficient browsing. Other sites might be organized based on an analysis of user roles.Designers who must choose among these schemes must also choose appropriate analyses to inform the design of the web site structure. These include item clustering techniques, user performance at finding documents using various prototype web site organizations, studies of user roles and tasks, and analyses of the material to be included in the site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.027 |
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".