Bibliographic record
Abstract
Handwriting is a complex visual pattern that is individualistic to the writer, much in the same way that faces are complex patterns that contain an identity. Due to the ubiquity of handwriting as a communication tool, we wanted to investigate sensitivity to handwriting using perceptual techniques. In this experiment, we investigated whether observers could reliably detect gender differences in handwriting samples and whether any sensitivity to gender was affected by stimulus inversion. We measured sensitivity in four stimulus presentation conditions: (1) uppercase words presented in an upright position, (2) lower case words presented in an upright position, (3) uppercase words presented in an inverted position, and (4) lower case words presented in an inverted position. Our results suggest that: (1) Participants are more confident in rating the gender of the authors for stimuli presented in the upright position than in the inverted position for both uppercase and lowercase handwritten samples. (2) Participants are accurate when rating the gender of the authors regardless of the handwritten samples are in uppercase or lowercase form and are more sensitive to changes in female handwriting samples. (3) Participants are more sensitive to the gender of the authors in the upright lowercase condition compared to the upright uppercase condition. These findings suggest that handwriting perception and identification rely on the global configuration of the stimulus, which suggests that handwriting perception relies on mechanisms that operate similar to those that support face processing. Department: Psychology Faculty Mentor: Dr. Nicole Anderson
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".