Complexities of fonts in disfluent experiments
Bibliographic record
Abstract
This study focuses on how fonts selected from different families have been used to test for disfluency. The motivation and standard for choosing a particular font for an experiment are not yet clearly defined from past studies. Drawing on methods in a systematic review of 10 articles published between 2007 and 2020, this article shows that authors prefer to use sans serif fonts in fluent conditions and serifs, scripts or handwritten fonts in disfluent conditions. In this study, disfluency manipulations were limited to reducing font sizes and percentages of grey or black. The largest size used was 56pt (fluent) and 18pt (disfluent) while the smallest was 12pt (fluent) and 10pt (disfluent). We observed that the opacity values of disfluent fonts ranged between 10% and 60%, making it unclear how disfluent a font can be. Apart from font sizes, fixation time, familiarity with materials and other controls influenced the results. This article reveals that a major gap still exists in research because of a lack of standard methods for determining the fonts used for testing subjects.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".