Towards a unified catalog of hypermedia design patterns
Bibliographic record
Abstract
There has been a recent increase in the number of published design patterns for hypermedia. Some of these patterns have been evolving, while others have remained untouched. This paper attempts to list all the patterns currently known, tracing the different publications in which they have appeared. The patterns are scrutinized and refined: some patterns are unified into one; some are deemed special cases of other patterns; some patterns are renamed. At the same time, we propose to rewrite the patterns in a vocabulary that is uniform, and to use similar pattern templates. We then discuss the creation of a design patterns system, which organizes the patterns and assists the designer in the process of recognizing the problems and their potential solutions. Finally we propose a subset of the patterns which should conform a catalog of basic patterns; this catalog will attempt to address the most common problems found during the design of hypermedia applications.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".