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
Adaptive interfaces have received a lot of criticism as adaptation and automatic assistance contradict with the principles of direct-manipulation interfaces. In addition to this, their success highly depends on the ability of user models to capture the goals and needs of the users. Since the construction of user models is often based on poor evidence, even the most advanced learning algorithms may fail to accurately predict the user goals. Previous research has not put much effort on investigating the usability problems that adaptive systems engage and developing interaction techniques that could resolve these problems. This paper presents an interaction model for Adaptive Hypermedia which merges adaptive support and direct manipulation. This approach is build upon a new content adaptation technique which derives from fisheye views. This adaptation technique supports incremental and continuous adjustments of the adaptive views of hypermedia documents and balances between focus and context. By combining this technique with visual representations and controllers of user models, we formed a twofold interaction model which enables users to quickly move between adaptation and direct control.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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