Youth Work in Croatia: Collecting Pieces for a Mosaic

This article explores the historical development of youth work in Croatia. By drawing from available data and personal experience, we describe three key phases of youth work development in a post-conflict country: (a) the period of the early 1990s as a “direct peace building" youth work; (b) the rise of nonformal education during the mid and late 1990s; and (c) the growth of a networked youth sector and its focus on youth policy advocacy starting in 2000. In addition, we refer to today's context, particularly because of its project-management orientation. Such categorization highlights various practices that we consider to represent youth work in a specific and contested national framework. Work with young people with fewer opportunities is being presented as a case, building on our observation that contemporary youth work continues to be embedded in civil society development and nonformal education, facing challenges of funding-driven discourse and unsystematic support.     

The relation between rate of growth and level of development

The paper tests the hypothesis that countries pass through three successive phases of development: an initial phase of stationary or slow growth; a phase when the growth rate is increasing (accelerating growth), and finally, a phase of decreasing (decelerating) growth. Since the regressions turn out to be significant at the 0.1 per cent level, it may be taken that the hypothesis is confirmed. An explanation is offered and deviant cases are discussed. One of the consequences of the established regularity is that the gap is widening between the least developed and other countries, but the countries at the intermediate level of development are catching up. Thus the usual twofold classification of countries will have to be replaced by a threefold one: less developed, developing and developed countries.     

The measurement of open apices of teeth to test chronological age of over 14-year olds in living subjects

Age determination in living subjects is a problem of increasing interest in our community, due to the increasing numbers of individuals without identification papers, who have immigrated illegally or committed crimes, and for whom it is necessary to verify whether they have reached the age of 14 years in order to be charged legally. Although the most widespread methods for age estimation refer to skeletal or dental analysis, these methods do present some drawbacks for identification of the age of 14. The aim of the present study is to discriminate between children who are or are not 14 years of age or older by measuring the open apices of teeth. We evaluated the OPGs of 447 persons aged between 12 and 16 years, of Italian, Croatian and Slovenian nationality. For each individual, dental maturity was estimated using the number of the seven left permanent mandibular teeth with root development complete, and normalized measurement of the open apices of the third molar. The results revealed that an individual is considered to be 14 years of age or older if all seven left permanent mandibular teeth have closed apices and the normalized measurement of open apices of the third molar is lower than 1.1.     

Age estimation using carpals: study of a Slovenian sample to test Cameriere's method

Carpals are often used as age indicators. In a recent study, Cameriere et al. studied the use of the ratio between the total area of carpal bones and epiphyses of the ulna and radius (Bo) and carpals (Ca) as age indicators. The present study, of a sample of 158 Slovenian children and adolescents aged between 6 and 16 years, focused on analysing the best regression for age estimation. The regression model yielded the following equation: age=-3.411+0.942 g+20.927(Bo/Ca), and explained 91.6% of total variance (R(2)=0.916). The median of the absolute values of residuals (observed age minus predicted age) was 0.09 years, with a quartile deviation of 0.786 years, and a standard error of estimate of 0.658 years. Comparisons between the previous equation referring to Slovenian children and the equivalent linear equation proposed by Cameriere et al. did not reveal any significant differences between the intercepts and slopes of the two linear models. These results suggested a common regression model for both Italian and Slovenian samples. The common regression model, describing age as a linear function of gender and Bo/Ca ratio, yielded the following linear regression formula: age=-2.907+0.408 g+20.757(Bo/Ca). This model explained 86% of total variance (R(2)=0.86). The median of the absolute values of residuals (observed age minus predicted age) was 0.02 years, with a quartile deviation of 1.02 years and a standard error of estimate of 0.96 years.