Bibliographic record
Abstract
As criminal tactics become increasingly sophisticated, using advanced tools and technologies, the demand for more powerful analytical systems has surged. Although classical machine learning approaches have been widely used in criminal analysis, the exponential growth of data, driven largely by large language models (LLMs) and AI agents, has exposed their limitations. Quantum computing, rooted in the principles of quantum mechanics, presents a promising alternative that harnesses the behavior of atoms and electrons to perform complex computations. This study evaluates classical and quantum machine learning approaches for crime prediction, analyzing their efficacy in classifying criminal incidents. Classical models, such as logistic regression, XGBoost, random forest, and support vector machine, demonstrated robust performance, with ensemble methods such as random forest and XGBoost achieving particularly high effectiveness in classification tasks. In contrast, quantum models, including the variational quantum classifier, the quantum neural network, and the quantum support vector machine, showed promising theoretical advantages but lagged in practical performance due to current hardware. The findings highlight the need for further refinement of quantum techniques to bridge this gap and fully harness their potential for complex pattern recognition in criminal data analytics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".