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.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".