Bibliographic record
Abstract
Forensic document examiners are tasked daily with determining the authenticity of signatures. The majority of these signatures are found on a line, within a box or within text. A major concern with this type of examination is the presence of these lines, boxes and text, since they can pose a form of constraint resulting in variations to an individual’s natural signature. This study examined the effects of constraint on an individual’s signature with the use of a digitizing tablet and inking pen to measure both the dynamic and static characteristics of the signature. Forty participants ranging in age from 16 – 83 provided a series of signatures for a total of 2400. Each participant signed in the presence of five different constraints, mimicking actual Canadian Government forms, including: a 4.7 cm line, a 6 cm x1.2 cm box, a 4.8 cm x 0.96 cm box, a 6.4 cm length and 0.4 cm height space within text, the Adult General Passport Application box produced by Passport Canada and a blank sheet as a control. This study suggests that when constraint is introduced, the pen speed, pen jerk, overall length, ascenders and descenders all vary significantly from that of the unconstrained signature. Pen pressure was the only feature to not show significant difference in the presence of constraint. In addition to these dynamic characteristics, anomalies such as extra artefacts, variation in complexity, hesitations, health issues and signs of anxiety were observed. This study demonstrates the impact that constraint has on a signature and indicates to forensic document examiners the need to carefully consider and evaluate these variations in the examination process.
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 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 | 0.002 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".