Perception of letter glyph parameters for InfoTypography
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 advent of variable font technologies---where typographic parameters such as weight, x-height and slant are easily adjusted across a range---enables encoding ordinal, interval or ratio data into text that is still readable. This is potentially valuable to represent additional information in text labels in visualizations (e.g., font weight can indicate city size in a geographical visualization) or in text itself (e.g., the intended reading speed of a sentence can be encoded with the font width). However, we do not know how different parameters, which are complex variations of shape, are perceived by the human visual system. Without this information it is difficult to select appropriate parameters and mapping functions that maximize perception of differences within the parameter range. We provide an empirical characterization of seven typographical parameters of Latin fonts in terms of absolute perception and just noticeable differences (JNDs) to help visualization designers to choose typographic parameters for visualizations that contain text, as well as support typographers and type designers when selecting which levels of these parameters to implement to achieve differentiability between normal text, emphasized text and different headings.
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 | 0.001 |
| 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