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Straipsnis skirtas išsilavinimo kaip vieno iš bendrinės kalbos vaizdinio požymių integruotumo analizei pristatyti. Tyrimo pagrindas – mokslinio projekto „Bendrinės kalbos vieta mentaliniame lietuvių kalbos žemėlapyje“ duomenys. Tyrimas vykdytas dešimtyje punktų, kurie, atsižvelgiant į užimamą plotą, proporcingai pasirinkti žemaičių (3) ir aukštaičių (7) geolektinėse ir regiolektinėse zonose. Bendra tyrimo imtis – 1415 tiriamųjų. Medžiagos tipas – sąmoningosios jaunuolių nuostatos, gautos atlikus anoniminį anketavimą. Pagrindinis anketos klausimų ir užduočių blokas yra skirtas regioninio variantiškumo vertinimui, savo požiūrį į bendrinę kalbą tiriamieji atskleidė per mentalinį žemėlapį ir atsakydami į atvirąjį ketvirtąjį anketos klausimą, skirtą bendrine kalba kalbančio žmogaus asociatams išsiaiškinti. Straipsnyje pristatomo tyrimo tikslui pasiekti yra svarbi bendrinės kalbos vartotojo asociatų užduoties medžiaga. Analizuojant laikomasi nuostatos, kad varianto ir jo vartotojo bruožai paprastojo kalbos bendruomenės nario požiūriu iš esmės yra susiję. Plėtojant įžvalgas apie išsilavinimo kaip bendrinės kalbos požymio integruotumą Lietuvos jaunuolių vaizdiniuose, nuosekliai išskleidžiami visų dešimties tyrimo punktų duomenys, aiškinami juose fiksuoti bendrumai ir skirtumai. The paper analyses the integrality of education feature in the image of standard language constructed by ordinary members of a language community. A part of the material taken from the research project "The Position of Standard Language in the Mental Map of the Lithuanian Language" devoted to the analysis of geolinguistic competence of ordinary members of a language community makes the empirical basis for such inquiry. The geolinguistic competence research was carried out in ten areas across the entire territory of Lithuania. Its general sample is 1,415 respondents. The paper focuses on the data received during the so called associate task when the respondents had to name the major associates of a standard language user. Upon employing the "keywords" technique for the classification of the associate data, it was discovered that education was integrated into the standard language images both among highlanders and lowlanders. The education associate was verbalised by the categories of intelligence, education, learnedness, high intellect, knowledge, and other. Approximately one third of young lowlanders (i.e. around 32% respondents of Plungė geolect; 30.77% respondents of Mažeikiai geolect; 32.20% respondents of Kretinga geolect) pointed out education as the most distinct layer of the standard language image. The comparative analysis of the three areas of the Lowland background concerning an inclusion of the education element into the image of standard language revealed that the images distinguished by Kretinga youth and Mažeikiai youth were closer to each other. In the structure of their standard language conceptualisation, the education segment occupied the central position together with the city segment. Other features, such as contemporaneity, culture, (simply) Lithuanian, etc. operated as secondary. Education was integrated into the generalised images of standard language by representatives of all the young highlanders’ groups; yet this segment was not equally important to the respondents of all the groups. The highest percentage (51.90%) of the respondents of the West-eastern Highland regiolect centre distinguished education as a significant feature of the standard language image. The same attitude was verbalised by approximately one third of the respondents of the geolect zones of Biržai and Varėna (including Merkinė), the respondents of the North-western Highland regiolect centre (Šiauliai) and the border zone of the Eastern and Western regiolects of East Highland (Kupiškis). The attitudes of the Vilkaviškis young respondents were most exclusive among all the respondent groups representing the Highland linguistic background: the stereotypical evaluation of a standard language user which might include the education associate has not been spread more widely.

Kiekviena etninė kalba turi daugiau ar mažiau skolinių, tad jau XX a. pradžioje Hermannas Paulis sukuria pirmąją sistemą, mėginančią juos suklasifikuoti. Nors per pastarąjį šimtmetį buvo nuolatos bandoma surasti geriausią ir aiškiausią skolinių klasifikacijos schemą, nesunku pastebėti, kad pateikiami šių sistemų kriterijai yra labai panašūs ir persipinantys. Dėl to neretai susiduriama su sunkumais, bandant priskirti vieną ar kitą iš svetimos kalbos atėjusį žodį kuriai nors konkrečiai skolinių kategorijai. Ne mažiau neaiškumų kyla dėl skolinius įvardijančių terminų. Antai lietuvių kalboje greta sąvokos „skolinys“, vartojamos ir tokios kaip „svetimžodis“, „svetimybė“, „vertinys“, „barbarizmas“. Viena vertus, šitokia terminų gausa liudija kalbos turtingumą ir jos leksines galimybes bei padeda tiksliau apibūdinti skolinius. Kita vertus, ji įneša nemažai painiavos ir subjektyvumo vertinant lingvistinius reiškinius. Šiame straipsnyje buvo surinkti dažniausiai lietuvių kalboje vartojami ispanų kalbos skoliniai (ispanizmai) ir, juos susisteminus ir suklasifikavus į skirtingas grupes, sudarytas jų sąvadas. Darbe taip pat buvo aptarta gausi terminija, vartojama šiuolaikinėje lietuvių kalboje svetimos kilmės žodžiams apibūdinti, bei pagrįsta nuostata, kodėl straipsnyje buvo pasirinktas „skolinio“ terminas. Every ethnic language has a greater or smaller number of borrowings. The first endeavour to classify loanwords by H. Paul are observed at the beginning of 20th century. Although, in the last century, effort has been made to find a good and clear loanword classification scheme, it is noticed that systematisation criteria presented by various researches are very similar and intertwining. Therefore, any attempts to classify such words in certain categories are frequently confronted with difficulties. A similar phenomenon is noticed in a variety of terms used to refer to loanwords. In the Lithuanian language, along with the term “loanword” such terms as “borrowing”, “alien-word”, “calque”, “barbarism” can also be found. On the one hand, such great number of different terms demonstrates the richness of the language and its lexical potentiality and helps to describe loanwords more specifically. On the other hand, this introduces confusion and subjectivity into the analysis of linguistic phenomena. The goal of this paper was to collect commonly used Spanish language borrowings (hispanisms) in Lithuanian language, systematize as well as classify them into different categories, and create a list. In the article, a deeper look was taken into today’s terminology used to describe loanwords in the contemporary Lithuanian, as well as to justify the choice of the term “loanword”. The list produced by the research shows that hispanisms belong to different areas and have different usage possibilities. The great majority of borrowings are used in art and zoology, but they are also found in such areas as botany, chemistry, economy. But, despite the fact that these words are used in the natural language, not all of them are included in the Contemporary Lithuanian Language Dictionary or international scientific dictionaries, not all of them can be found in Lithuanian language corpora either. A small amount of direct and indirect Spanish language borrowings used in the Lithuanian language suggests considerable cultural differences and a weak historical interaction between the two societies. The absence of sufficient information about Spanish language borrowings in the Lithuanian language as well as the lack of scientific research in this field shows that this is an area which may offer researchers interesting and important discoveries.

Darbe pateikti švietimo duomenų statistinės analizės modeliai ir programinė įranga, kurie palengvina sprendimų priėmimą bei jų pagrindimą sprendžiant švietimo valdymo organizavimo uždavinius. Modeliai ir programinė įranga realizuoti naudojant taikomosios statistikos metodus, objektinio programavimo technologiją ir SAS sistemą., The statistical analysis models and software of education data for support the decisionmaking is presented in this paper. The models and software were developed using applied statistics methods, object programming technology and software SAS.