Determining Optimal Granularity for Effective Handwriting Analysis
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
Abstract In most literature on handwriting analysis, the datasets used are typically mentioned. However, the sizes of these datasets can vary once they undergo preprocessing for actual model training. This results in different sample sizes being used as inputs. We hypothesize that these varying input sizes can influence the output results of the models. In this paper, we explore the optimal granularity for handwriting analysis. We trained two deep learning models to classify traits such as extraversion (EXT) and conscientiousness (CON) using our own dataset. Our findings indicate that the optimal granularities are 3 × 8, 8 × 6, and 9 × 12 for different splitting patterns. We recommend selecting training samples with at least 77 instances, each containing 3 to 4 lines of text, to ensure robust model performance. These guidelines can serve as a reference for future research in handwriting analysis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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