Analysis of counterfeits and public survey results as design input
Bibliographic record
Abstract
Both Canada and the United States have undertaken recent studies of public knowledge and human perception related to bank notes and counterfeits. These two types of studies yield complementary results that confirm or refute accepted beliefs regarding security features and bank notes, and provide guidance in their design. However, to be useful, the results of these studies need to be evaluated and applied in the context of note design, such evaluation and incorporation of results can be used to improve how bank notes are designed for different users, such as members of the general public, cash handlers, bank tellers and law enforcement personnel. Analysis of the counterfeits used in the perception studies and comparative evaluation of results can lead to better understanding of types of counterfeiters and help identify gaps in note designs to address those types. In this paper, the results from two recent studies conducted by the Bank of Canada on public awareness of currency design features and on human perception of genuine and counterfeit notes are used to illustrate the method of analysis and application of results to bank note design. Interpretations for specific features are highlighted.
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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 | 0.047 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".