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
Tag clouds are a popular method for visualizing and linking socially-organized information on websites. Tag clouds represent variables of interest (such as popularity) in the visual appearance of the keywords themselves - using text properties such as font size, weight, or colour. Although tag clouds are becoming common, there is still little information about which visual features of tags draw the attention of viewers. As tag clouds attempt to represent a wider range of variables with a wider range of visual properties, it becomes difficult to predict what will appear visually important to a viewer. To investigate this issue, we carried out an exploratory study that asked users to select tags from clouds that manipulated nine visual properties. Our results show that font size and font weight have stronger effects than intensity, number of characters, or tag area; but when several visual properties are manipulated at once, there is no one property that stands out above the others. This study adds to the understanding of how visual properties of text capture the attention of users, indicates general guidelines for designers of tag clouds, and provides a study paradigm and starting points for future studies. In addition, our findings may be applied more generally to the visual presentation of textual hyperlinks as a way to provide more information to web navigators.
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 | 0.001 | 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