The use of a polygraph in law enforcement activities: the experience of foreign countries
Bibliographic record
Abstract
The article analyzes the experience of using polygraph and polygraph research in the activity of law enforcement agencies of foreign countries. The long-standing practice of using the polygraph in the activities of law enforcement bodies is by such countries as Japan, USA, Canada, Israel. It is determined that the main directions of the use of the polygraph are as investigations of offenses (including criminal) and the fight against organized crime, as well as verification of credibility and integrity of candidates for positions in the police, prosecutor's offices, courts and other law enforcement agencies, as well as civil servants who want to take higher positions. Polygraph tasks include: narrowing the range of suspects, establishing the fact of committing a crime, creating conditions for obtaining true testimonies, collecting additional and guiding information that can help to choose the most promising and substantiated direction of investigation. Effectiveness of the use of the polygraph in the activities of law enforcement agencies, convincingly proved practice in many countries. These technologies have proved their worth and have been used successfully for a long time in the US, Israel, Turkey, Poland, the Baltic States and other countries, in particular, during the selection of a certain group of civil servants, conducting internal investigations, in particular in pre-trial investigations, disclosure and investigation of resonance crimes. The normative-legal principles of the use of psychophysiological researches using polygraph in the national practice are described. Summing up, it should be noted that the long-standing practice of using a polygraph in the activities of law enforcement bodies is the countries such as Japan, USA, Canada, Israel. The main tasks that are solved with the help of a polygraph are: narrowing the range of suspects, establishing the fact of committing a crime, creating conditions for obtaining true testimonies, and collecting additional information about the offense under investigation. Often, the results of such studies are used not for the purpose of obtaining evidence, but for the collection of orientation information that can help to select the most promising and substantiated direction of investigation. Foreign experience shows that, in law enforcement practice, checks on polygraphs are usually used not in the interests of obtaining judicial evidence for a decision on a case, but to assist the investigator in choosing a more perspective and something grounded in his direction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".