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
The settings panel is the standard customization mechanism used in software applications today, yet it has undergone minimal design improvement since its introduction in the 1980s. Entirely disconnected from the application UI, these panels require users to rely on often-cryptic text labels to identify the settings they want to change. We propose the Anchored Customization approach, which anchors settings to conceptually related elements of the application UI. Our Customization Layer prototype instantiates this approach: users can see which UI elements are customizable, and access their associated settings. We designed three variants of Customization Layer based on multi-layered interfaces, and implemented these variants on top of a popular web application for task management, Wunderlist. Two experiments (Mechanical Turk and face-to-face) with a total of 60 participants showed that the two minimalist variants were 35% faster than Wunderlist's settings panel. Our approach provides significant benefits for users while requiring little extra work from designers and developers of applications.
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 | 0.001 |
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