Current problems of psychology in the field of law enforcement: concepts, approaches, technologies (Vasilievsky readings - 2023) (analytical review of international research-to-practice conference)
Bibliographic record
Abstract
The article presents an overview of the research-to-practice conference «Current problems of psychology in the field of law enforcement: concepts, approaches, technologies» (Vasilievsky Readings - 2023), which is dedicated to the memory of the Soviet scientist-psychologist, Doctor of Psychological Sciences, Professor, Honored Scientist of the Russian Federation Vladislav Leonidovich Vasiliev. The research results of scientific schools of the Russian Federation, People’s Republic of China, the Kyrgyz Republic, the Republic of Armenia, the Republic of Belarus, the Republic of Kazakhstan, the Republic of Uzbekistan on the following problems were presented at the conference: improvement of psychological work in the system of moral and psychological support for the activities of internal affairs bodies; introduction of achievements of psychology in practice of crime detection and investigation; psychological support for the implementation of law enforcement officers operational and service tasks, including in special conditions; protection of law enforcement officers from destructive informational and psychological impact. The results of the conference demonstrate the need to combine the efforts of various scientific psychological schools from different countries to achieve law enforcement goals, protect public order, ensure public safety and well-being of people around the world.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".