mSpace: interaction design for user-determined, adaptable domain exploration in hypermedia
Bibliographic record
Abstract
Adaptive Hypermedia systems have sought to support users by anticipating the usersí information requirements for a particular context, and rendering the appropriate version of the content and hypermedia links. Adaptable Hypermedia, on the other hand, takes the approach that there are times when adaptive approaches may not be feasible or available and that it would still be appropriate to facilitate user-determined access to information. For instance, users may come to a hypermedia and not have a well-defined goal in mind. Similarly their goals may change throughout exploration, or their expertise change from one section to another. To support these shifting conditions, we may need affordances on the content that a solely adaptive approach cannot best support. In this paper we present an interaction design to support user-determined adaptable content and describe three techniques which support the interaction: preview cues, dimensional sorting and spatial context. We call the combined approach mSpace. We present the preliminary task analysis that lead us to our interaction design, we describe the three techniques, overview the architecture for our prototype and consider next steps for generalizing deployment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".