A framework for unifying presentation space
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
Making effective use of the available display space has long been a fundamental issue in user interface design. We live in a time of rapid advances in available CPU power and memory. However, the common sizes of our computational display spaces have only minimally increased or in some cases, such as hand held devices, actually decreased. In addition, the size and scope of the information spaces we wish to explore are also expanding. Representing vast amounts of information on our relatively small screens has become increasingly problematic and has been associated with problems in navigation, interpretation and recognition. User interface research has proposed several differing presentation approaches to address these problems. These methods create displays that vary considerably, visually and algorithmically. We present a unified framework that provides a way of relating seemingly distinct methods, facilitating the inclusion of more than one presentation method in a single interface. Furthermore, it supports extrapolation between the presentation methods it describes. Of particular interest are the presentation possibilities that exist in the ranges between various distortion presentations, magnified insets and detail-in-context presentations, and between detail-in-context presentations and a full-zooming environment. This unified framework offers a geometric presentation library in which presentation variations are available independently of the mode of graphic representation. The intention is to promote the ease of exploration and experimentation into the use of varied presentation combinations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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