The International Training Centre of the International Labour Organization (ITCILO) is the training arm of the International Labour Organization (ILO). It runs training, learning and capacity development services for governments, employers' organizations, workers' organizations and other national and international partners in support of Decent Work and sustainable development. It is part of the United Nations System.
Non-formal education, actively developing, has already raised a demand to become officially recognized and receive regulations from the authorities. This process began in the 90s of the last century in European countries, and in the Republic of Armenia it has perhaps received its manifestations recently. Therefore, the assessment of the expectations of various beneficiaries of non-formal education and the assignment of qualitative characteristics, which was carried out in the article, is of great importance. In addition, by studying the experience of the motives of non-formal education, a classification of the reasons of the development of this form of education has been proposed.
Financing non-formal education requires special approaches. This form of education accompanies an individual throughout his or her life and, therefore, can lead to financial obstacles in the event of payments. However, on the other hand, in addition to individual students, non-formal education also has public and private beneficiaries who can financially support the sustainable development of supplementary education. The article discusses the responses to non-formal education in the legislative field of the Republic of Armenia and, based on this, makes proposals for strengthening the legislative norms for the establishment of the financing mechanisms for this form of education. In particular, it is proposed to legislatively form a national fund for financing non-formal education on the principle of participation of beneficiaries, which ensures the availability of financial resources for organizing additional education in the country.
To better understand the consequences of stress in realistic scenarios, police cadets were tasked with performing a police intervention under differing expectations. One group was led to anticipate a dangerous mission, while the other expected a routine event. In the field, however, both groups faced the same challenging situation. The warned group exhibited strong pre-intervention stress responses, which was minimal in the other group. By contrast, the unwarned group experienced a sudden surge in stress within the first minute of the intervention, as reality clashed with their expectations. A similar sudden stress response by the beginning of the intervention was missing from the warned group. A significant portion of cadets unlawfully attacked suspects, a behavior linked to intense stress displayed at the onset of the intervention. This emotional, illegitimate aggression was driven primarily by the noradrenergic stress response, with no indication of cortisol involvement. Traditional statistical methods (group comparisons, univariate, and multivariate regressions) suggested that psychological traits had little impact compared to acute stress effects. However, machine learning revealed that psychological characteristics—such as those assessed by the Reactive–Proactive Aggression Questionnaire, Buss–Perry Aggression Questionnaire, Big Five Personality Test, and Barratt Impulsiveness Scale—played a crucial role in conjunction with stress responses. Multivariate analyses yielded data similar to those obtained through machine learning, but only when the dependent variables were selected to match those identified as crucial by the latter. These findings highlight the power of machine learning in uncovering complex interactions that traditional methods might overlook.
