MAIN DIRECTIONS OF FOREIGN SCIENTIFIC RESEARCH IN THE FIELD OF CRIMINOLOGICAL FORECASTING
Bibliographic record
Abstract
The article analyzes the development of foreign scientific approaches to forecasting in the field of criminal law regulation. The materials of the main scientific works of foreign scientists related to the problems of forecasting in the USA, Great Britain and Canada are used. It is established that initially the study of forecasting tools was in demand by the penitentiary system for making decisions on parole. The earliest and most famous works appear as early as the 1920s. They initiated the use of actuarial forecasting techniques in the criminal justice system. Another area of research was the projection of the prison population, which was due, inter alia, to the problem of overcrowding. Later, works devoted to predictive policing and the use of analytical methods for predicting crime began to appear. This was due to the experience of successful implementation of special software in police practice. A great demand for scientific research on predictive analytics arose after the terrorist attacks of 2001, when research teams began to receive additional funding. It was necessary to develop modern computing systems for risk assessment and spatio-temporal analysis in order to counter crime, including terrorism. In addition, the increase in the scale and level of detail of data available to law enforcement agencies and the automation of their collection contributed to the intensification of scientific activity. Foreign researchers have borrowed forecasting methods that have found their application in other sciences or fields of activity. The expediency of using certain methods is the subject of a broad scientific discussion, which currently concerns, first of all, the admissibility of obtaining personal data and the problems of algorithmization of activities.
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 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".