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Ph.D. in Physics and Mathematics, Associate Professor of the Theory of Law and Constitutionalism Department, Faculty of Law,

West Ukrainian National University, Ternopil, Ukraine. WoS Researcher ID: H-3889-2017

Ph.D., Associate Professor of the Department of Security and Law Enforcement, Faculty of Law, West Ukrainian National

University, Ternopil, Ukraine. WoS Researcher ID: G-5615-2017

Ph.D. in Technical Sciences, Senior Teacher, National Academy of the Security Service of Ukraine, Kyiv, Ukraine.

WoS Researcher ID: KOC-0812-2024

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The crime rate is steadily increasing in many countries, causing serious concern in society and posing new

challenges for law enforcement and justice systems (Gruszczyńska & Gruszczyński, 2023). This trend

poses a serious threat to public safety and negatively affects economic development (Galinari & Bazon,

2021; Anser et al., 2020; Adela & Aldhaheri, 2024) undermines citizens' sense of security, and causes a

decline in trust in law enforcement and the judicial system (Kulachai & Cheurprakobkit, 2023). The current

trends require a comprehensive analysis of the reasons for such negative dynamics and the development of

effective ways to counteract this phenomenon at the international and national levels. At the same time, the

fight against crime requires a comprehensive approach, which includes not only increasing the efficiency

of law enforcement agencies but also taking into account the "prison paradox", according to which an

increase in the number of prisoners does not have a significant impact on reducing crime and causes

reduces the burden on the penitentiary system and opens the way for these people to return to a law-abiding

society. The issue of giving a chance for correction to certain categories of offenders is relevant and justified

(Letlape & Dube, 2023). Applying rehabilitation programs, psychological support, vocational training, and

involvement in socially useful work to them, provided that they sincerely repent and desire to be corrected,

may be a more effective approach than simply isolating them. This will save resources and at the same time

preserve the chance for a dignified life for those who can realize their mistakes (Legodi & Dube, 2023). At

the same time, the approach to hardened, incorrigible criminals should be strict and uncompromising, as

they have consciously chosen the illegal path and pose a significant threat to public safety. They should be

subject to the strictest measures by the law. Distinguishing between offenders and taking an individual

approach to each case, taking into account the level of danger they pose to society, is justified and necessary

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in terms of humanity and common sense. Giving a chance for correction to those who can get on the path

identifying risk factors, the level of threat to society, and developing individual approaches to rehabilitation

and resocialization.

An objective assessment of the level of danger that a suspect (convict/prisoner) poses to society is an

important element in ensuring the rule of law, justice, a balance of interests, and increasing the efficiency

of the judicial system. Such an assessment is based on a comprehensive analysis of various individual

characteristics to determine a person's propensity to repeat illegal actions, their social adaptability, and the

possibility of successful resocialization after release. The obtained information can help the court impose a

punishment that corresponds to the degree of public threat posed by the committed crime and the personality

of the offender. This contributes to the realization of the principle of justice as a fundamental principle of

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same criteria to all cases, unlike human expert analysis, where deviations are possible. Analytical models

M. Gruszczyński evaluated the relationship between crime rates and the number of prisoners in European

countries based on a correlation-regression analysis of four types of offenses: assault, rape, robbery, and

theft. The researchers found that the level of prison occupancy is directly related to the peculiarities of the

state's criminal law policy, in particular, the harshness or liberalism in matters of choosing the measure of

punishment and determining the terms of imprisonment for offenders Gruszczyńska & Gruszczyński,

2023). S. Caridade et al., analyzed the individual and social environment associated with criminal activity

(Caridade, 2022). K.M. Berezka et al. found that early involvement in the criminal environment is a

significant risk factor for committing repeated offenses (Berezka et al., 2022). Many studies on identifying

non-obvious signs associated with a person's future criminal activity and decision-making regarding crime

prevention specifically concern crimes committed in adolescence. Aazami et al., conducted a literature

functioning and types of criminal behavior, as well as in psychosocial risk/protective factors associated

with each profile. The results obtained can contribute to improving the assessment necessary for

informational support of court decisions (Galinari & Bazon, 2021).

The issue of assessing the level of risk that criminals pose to society, imposing fair punishment, and

effective alternatives to incarceration is of universal importance and requires comprehensive

interdisciplinary study, taking into account global trends, international experience, and the latest

achievements in the fields of psychology, jurisprudence, criminology, the penitentiary system, offender

rehabilitation, public safety, and the applied use of statistical methods and information technologies. Only

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such comprehensive and systematic research can provide answers to pressing challenges and offer balanced

judiciary. One such important decision is determining the level of danger posed by a convict, which ensures

a balance between protecting society, implementing the principles of fair justice, and successful

reintegration of offenders after serving their sentences.

To create an analytical model for assessing the level of danger posed by criminals to society, discriminant

canonical analysis was used (Boedeker & Kearns, 2019). This is a statistical method used to predict the

belonging of objects or observations to certain groups or categories based on a set of measured variables.

Its main goal is to find a linear combination of independent variables (a discriminant function) that best

separates or discriminates between groups. There are several predefined groups or categories to which

objects belong. There is a set of independent variables (predictors) that are measured for each object. A

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(Boedeker & Kearns, 2019).

A dense correlation (0.87) is identified only for one pair of variables – AAS and AAS. This means that the

earlier a person was involved in the criminal environment (was sentenced to probation or a real measure of

punishment), the earlier they ended up in penitentiary institutions. Usually, for a crime that is not serious

and committed by a person for the first time, convicts receive a suspended sentence. Therefore, the dense

correlation between AAS and AAS may indicate that such offenders commit repeated offenses.

The purpose of the empirical analysis is to find a linear combination of the studied independent variables

that best distinguishes between groups of convicts according to the level of danger they pose to society.

The Wilks' Lambda value of 0.154  [0; 1] and close to 0 (Table 3) means that the discrimination is good.

Discriminant Function Analysis Summary

function. The predictors AGE, AAS, TE, EL, SC, ED, and MD have high statistical significance (p < 0.01).

SEX, AAP, PR, and MS (p > 0.05) are not significant for the distribution of convicts into groups according

to the level of danger they pose to society. Both Wilks' Lambda and Partial Lambda estimates can take

values ranging from 0 to 1. Wilks' Lambda = 0 means complete discrimination, and Wilks’ Lambda = 1

means no discrimination. The closer the Partial Lambda value is to 1, the smaller the contribution of the

corresponding variable to the discrimination model. The closer this value is to 0, the greater the contribution

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of the corresponding variable to the discrimination model. The variable SC has the highest weight for

F-remove is a statistical measure used to assess the importance of individual predictors (independent

variables) in a discriminant model. A high F-remove value for a particular predictor indicates that this

predictor makes a significant contribution to the discrimination between groups in the discriminant model,

i.e., it is important for classifying observations. A low F-remove value indicates that the corresponding

predictor has little influence on classification, and it can be safely removed from the model without

significant loss of discriminatory ability. The highest value among all variables F-remove = 7440.73 is for

SC. This confirms its greatest influence on discrimination.

Table 4 presents the classification matrix for verifying the correctness of the training samples.

p = 0.32

assigned to the identified groups based on the level of danger they posed to society (Table 4). However, the

squared Mahalanobis distances of these objects to the groups they were assigned to are smaller than the

distances to the centers of other groups (Table 5). For example, for object 8, the squared Mahalanobis

distance to the “high” group it was assigned to is 16.11. It is smaller than the distances to the centers of

other groups – 16.17 to the "moderate" group and 34.04 to the “low” group. Therefore, the classification of

these objects into the previously identified groups is correct. There is no reason to exclude these objects

from the analyzed sample.

Table 5.

Squared Mahalanobis Distances from Group Centroids (fragment)

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critical value 2(24) = 10.856. Therefore, there is a strong relationship between the discriminant function

and the identified groups of danger that convicts pose to society (Table 6).

Table 6.

Chi-Square Tests with Successive Roots Removed

Classification Functions; grouping: RR

model is presented as follows:

high = -32.69 + 7.73 AGE + 0.30  AAS + 0.19  TE + 0.34  EL + + 2.63  SC + 2.36  ED + 8.00 

moderate = -27.10 + 6.33  AGE + 0.28  AAS + 0.31  TE + 0.34  EL + 1.41  SC + 1.44 ED + 8.22

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groups, respectively) are the most different for different groups. An increase in the number of suspended

lower level of danger that a convict poses to society. These results confirm the estimates obtained by other

authors (Onyeneke & Karam, 2022; Ades & Mishra, 2021). Employment has a greater impact on the

distribution of objects into the “low” group: individuals who have a permanent job pose less danger to

others. Similar conclusions were drawn by other researchers (Zungu & Mtshengu, 2023). The motivation

for dismissal does not significantly affect the distribution of prisoners into the identified groups, but it is

more inherent in individuals who pose less threat to society. This issue has not been studied in the literature,

so it requires additional attention and further detailed analysis.

The obtained discriminant model is a system of linear equations (linear combinations of independent

variables) that will optimally distribute convicts (suspects) into the corresponding groups (high, moderate,