Hur personer tänker, tolkar och förstår situationer påverkar hur lyckliga de är. Syftet med uppsatsen är att utifrån ett socialkonstruktivistiskt perspektiv undersöka hur individer konstruerar lycka. Detta har gjorts genom att undersöka hur individer konstruerar  positiva definitioner av situationer och hur negativa definitioner kan göras om till mer positiva. Uppsatsens empiri utgörs av filmen Love actually, som det har utförts en kvalitativ analys av. Social interaktion visade sig genomgående vara centralt för hur individer konstruerade sina definitioner av situationer – positiva så väl som negativa. Andra personer kan erbjuda alternativa definitioner och försöka övertyga om att dessa är korrekta. Speciellt signifikanta andra har stort inflytande över hur individer tolkar situationer. Det framgick även att personer utgår från och använder den sociala verkligheten och dess institutioner och symboler för att bygga upp och motivera sina definitioner och omdefinitioner av situationer. För att konstruera positiva definitioner eller omdefinitoner av situationer, och därigenom öka sin egen eller en annan persons lycka via social interaktion, måste hänsyn tas till hur den sociala verkligheten är konstruerad, man måste alltid förhålla sig till denna.

Enligt Goffmans dramaturgi får en social situation sin definition av att aktörer genom framträdanden presenterar sin förståelse av den. Syftet med detta magisterarbete är att utveckla en teori om hur människor tolkar sådana framträdanden, samt att pröva några av den nya teorins teser empiriskt. Det teoretiska arbetet har en hermeneutisk karaktär, där idéer från symbolisk interaktionism och anknytningsteori tolkas, omtolkas och korsbefruktas. Den empiriska delen är kvantitativ och använder ett frågeformulär. Den nya teorin förklarar människors strävan efter en gemensam definition som en minimering av risken att känna förödmjukelse inför självet. Teorin beskriver också ett sökande efter uppmärksamhet och en strävan efter att klara sig själv som två riskminimerande strategier. Vidare är de strategier som människor använder avgörande för hur de tolkar framträdanden. Dessutom finns en koppling mellan människors tolkningar och deras anknytningsstilar. Det sistnämnda undersöks empiriskt, dock utan framgång. Slutsatsen är att frågeformuläret behöver vidareutvecklas.

BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

Denna undersökning syftar till att undersöka hur lärare förhåller sig till, definierar och undervisar i grammatik med de yngre åldrarna och de svårigheter som lärarna stött på i sin grammatikundervisning. Denna undersökning baseras på intervjuer med sex behöriga lärare i årskurs 1–3, varav en speciallärare som tjänstgjorde på samma skola i förskoleklass. Som komplement till den del av lärarintervjuer gjordes också en översiktlig läromedelsanalys utifrån läromedel som lärarna uppgav att de använde i grammatikundervisningen. Grammatik och grammatikundervisningen är ett omdiskuterat ämne, även om diskussionen inte är så uttalad när det gäller undervisning på lågstadiet. Sammantaget visar undersökningens resultat att de deltagande lärarna ser begreppet grammatik som ett diffust begrepp och det definieras också olika av lärarna. Det framkom även stora variationer när det gällde lärarnas attityd till begreppet grammatik och grammatikundervisning som något svårt och tråkigt eller som något som de anser är enkelt och meningsfullt. Däremot råder samstämmighet hos deltagarna om att grammatik och grammatikundervisning är viktigt för elevernas språkutveckling. Läromedelsanalysen visade att det i vissa läromedel tydligt framgick grammatiska inslag och grammatiska begrepp användes, medan det i andra läromedel inte gick att urskilja de grammatiska inslagen. / The purpose of this survey is to examine the way teachers relate to, define and teach grammar to pupils of the younger grades and the difficulties they encounter in teaching grammar. The research is based on interviews carried out among six licenced teachers, all employed in the same school and of whom one is a special education teacher. They teach classes in pre-school and primary school, grade 1¬3. Complimentary to the interviews, a general analysis of the teaching material used in their teaching, was carried out. Grammar and grammar teaching is a widely discussed topic, even if little is said about grammar teaching in the younger years. Altogether, the results of the study show, that the teachers taking part in the study regard grammar as a vague and unspecific term and the teachers define the concept of grammar differently. Another conclusion was that the attitudes towards grammar among the teachers varied widely. Some consider grammar difficult and boring while others believe it to be meaningful and easy to grasp. On the other hand, the teachers taking part in the survey agreed upon the importance of grammar and grammar teaching in the linguistic development of the pupils. The analysis of the teaching materials showed that some materials cover grammar as an clearly defined topic and formal grammatic terms were consciously used, while in others formal grammar as a topic was less evident.

BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

No description available.

Decisions on measures reducing environmental damage or improving environmental impact are usually constrained by financial limitations. Eco-efficiency analysis has emerged as a practical decision support tool by integrating environmental and economic performance. Environmental impact, as well as economic revenues and expenses, are usually distributed over a certain time scale. The temporal distribution of economic data is frequently assessed by discounting while discounting of environmental impact is rather uncommon. The scope of this paper is to reveal if this assumed inconsistency is common in eco-efficiency assessment literature, what reasons and interrelations with indicators exist and what solutions are proposed. Therefore, a systematic literature review is conducted and 35 publications are assessed. Theoretical eco-efficiency definitions and applied eco-efficiency indicators, as well as applied environmental and economic assessment methods, are compared here, but it is revealed that none of the empirical literature findings applied or discussed environmental discounting. It was, however, found in methodical literature. It is concluded that the theoretical foundation for the application of discounting on environmental impact is still insufficient and that even the theoretical foundation of economic discounting in studies is often poor. Further research and, eventually, a practical framework for environmental discounting would be beneficial for better-founded, more “eco-efficient” decisions.

No description available.

No description available.