1,721,787 research outputs found
Socially Responsible Investments. The Crossroads Between Institutional and Retail Investors
No full textDe La Torre, M G, 402590
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/381160Surname: DE LA TORRE. Given Name(s) or Initials: M G. Military Service Number or Last Known Location: 402590. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 47567.195858
Item: [2016.0049.13453] "De La Torre, M G, 402590
Classificazione automatica di narrazioni autobiografiche
No full textL’insegnante, può rappresentare per l’alunno, in diversi momenti, una base sicura, un interlocutore paritario o unantagonista. A tali diversi atteggiamenti, in una visione evoluzionista corrispondono le motivazioni sociali all’accudimento, cooperazione e agonismo, che si è inteso analizzare attraverso l’analisi testuale di frammenti autobiografici. A tale scopo è stato chiesto a 229 soggetti di narrare un episodio di relazione con l’insegnante della scuola elementare. I testi sono stati classificati, attraverso un nuovo metodo di classificazione automatica degli SMI e delle emozioni ad essi associati in base a vocaboli-chiave.
Calcolando il prodotto tra la matrice F (subfrequenze) e la matrice I (indicazione), si è ottenuta una matrice K~ (racconti x categorie), normalizzata, dividendone i valori per i totali di riga. Nella risultante matrice di classificazione sfumata K, il generico elemento k(i,j) è interpretato come probabilità di appartenenza del racconto i-esimo alla categoria j-esima. Le colonne della matrice di classificazione K sono state interpretate come funzioni caratteristiche di insiemi sfumati (le categorie “imprecise” o”sfocate” della classificazione automatica), dei quali sono state calcolate misure di sfocatura, la cui media indica il grado di imprecisione (incertezza).For a student the teacher represents in different moments a safe base, a peer or an antagonist speaker. In an evolutionist view, three different social motivations correspond to these attitudes: caregiving, cooperative and competitive motivations, which we have explored through the textual analysis of autobiographic fragments. We have asked 229 students to write about an anecdote regarding their interaction with a primary school teacher. The texts where later classified from the perspective of the Interpersonal Motivational Systems (SMI).
We have experimented a method of automatic classification of SMI (and the corresponding emotions) R (agonistic motivation) A (attachment/caregiving) and P (cooperation). We set up some suitable matrices to represent the correspondence (deterministic or probabilistic) between keywords and psychological categories. The product between F (subfrequencies matrix) and I (lexical indication matrix) gives us two classification matrices according to the “fuzzy sets”. We interpret the generic element k(i, j) of these matrixes as the probability of belonging to the i-th report and the j-th category. Comparing this automatic classification with a manual one gives us different percentages of concordance: 73% R, competition, 70% A attachment/caregiving, 47% P, cooperation
Seminari di filosofia del diritto: categorie del dibattito contemporaneo.
No full textcapp. I (Uguaglianza, 9-29), III (Felicità, 81-101), IV (Comunità, 103-25), V (Moralità, 127-49
Introduction
No full textThe aim of this chapter is to introduce the aim and structure
of the book. Speci!cally, the aim of the book is to build a bridge between
corporate social responsibility (CSR) and sustainable !nance in !nancial markets. Classic CSR topics have been investigated in the light of a
modern conception of sustainability. The book is organized in two main
blocks. The !rst block emphasizes four relevant topics in the CSR panorama of !nancial institutions: banks remuneration practices; human capital disclosure; the impact of environmental performance on banks, and
!nally, the institutional investors’ attitude towards socially responsible
investments (SRIs). The second block looks to CSR practices within the
!nancial markets and discusses risk-return pro!les of SRI and non-SRI
indexes in different time frames; it investigates whether thematic social
responsible funds obtain different risk-return than traditional funds,
and !nally, assesses whether equity crowdfunding could foster social
innovation
Delayed benign surgery during the COVID-19 pandemic. The other side of the coin
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Do Impact Investments Contribute to Portfolio Performance? A Preliminary Investigation
Full text linkSocial Impact Investments (SII) intentionally generate social impact and financial return. Portfolio diversification is one of the under-investigated areas in SII literature. The aim of this paper is to fill this gap by conducting a preliminary investigation of social impact firms (SIF) contribution to portfolio risk and performance. For the purpose of this paper, we use a sample of SIF members of the London Social Stock Exchange who are publically listed and two contrast samples with traditional firms (non-SIF). To carry out the analysis, we employed methodology based on Markowitz (1952a, 1952b) and Sharpe (1963). The paper may provide useful insights for asset managers and investors involved in portfolio choice evaluation and policy makers interested in fostering development of the social impact market
Anti-Money Laundering in Italian Banks
No full textAims: This paper focuses on some relevant aspects of a banking anti-money laundering (AML) system (training, information and regulatory development) in order to understand their impact on the enhancement of the customer risk assessment process.
Study Design: Research based on questionnaire.
Place and Duration of Study: Italy, between September 2012 and September 2013.
Methodology: We sent questionnaires to the 75 Italian banking groups listed in the Bank of Italy Register at March 2012. Over 60 per cent of the total participated to the survey.
Results: Training, information and regulatory development represent important points in the AML systems of the banks of the sample. They show a strong commitment in the
development of the (Know Your Customer process), in order to fight against money laundering; however weaknesses are still present and improvements are needed
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