Bibliographic record
Abstract
Handwriting is a very important form of human communication. As a stimulus, 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 traditional psychophysical techniques. In this experiment, we will investigate whether observers’ sensitivity to gender differences in the authors of the handwriting scales with different manipulations to the stimulus. We will measure sensitivity in 4 conditions: (1) uppercase words in an upright position, (2) lower case words in an upright position, (3) uppercase words in an inverted position, and (4) lower case words in an inverted position. Our first goal is to determine whether participants are sensitive to differences in gender within handwriting. If so, we would also like to investigate if those differences extend to upper and lowercase samples as well as upright and inverted. We expect to see a difference in perception in all 4 conditions. This would then support the idea that the process of word-recognition is similar to the process of other complex visual stimuli such as faces. For future direction, we hope to conduct other form of manipulation to individual letters of handwriting to study the individuality within the handwriting stimuli. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Nicole Anderson Department: Psyhology
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".