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Gregor von Nazianz, Über Vorsehung, Peri pronoiasSchwab, Andreas Gregorius January 2009 (has links)
Zugl.: München, Univ., Magisterarbeit
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Origenes und die "Simplices". Theologie vor den Herausforderungen einfachen Glaubens / Origen and the "simplices". Theology confronted with the challenge of simple faithGrillmayer, Ralf January 2009 (has links) (PDF)
Origenes, der bedeutendste Theologe der griechischen Kirche, setzt sich in seinem umfangreichen Gesamtwerk immer wieder mit den so genannten simplices auseinander, der Masse der einfachen und ungebildeten Christen, die seiner Theologie, insbesondere seiner Schriftauslegung, nicht folgen können oder wollen. Die Forschung der beiden letzten Jahrhunderte zeichnet in der Regel ein Bild großer gegenseitiger Abneigung bis hin zu offenen Feindseligkeiten, die dem berühmten Wissenschaftler von seinen Gemeinden in Alexandria und Cäsarea entgegengebracht wird. Die Untersuchung behandelt die einschlägigen Texte nicht nach einem bestimmten theologischen Raster, sondern kommt durch deren Interpretation aus ihrem jeweiligen Kontext heraus zu Ergebnissen, welche die Persönlichkeit des berühmten Intellektuellen, Theologen, Meisters des geistlichen Lebens und Seelsorgers in einem neuen Licht erscheinen lassen. Insbesondere erlaubt eine chronologische Anordnung des Stoffes in der einigermaßen gesicherten Reihen-folge der Hauptschriften den Rückschluss, dass bei Origenes eine Entwicklung hin zu einem größeren Verständnis für die Ungebildeten in der Gemeinde stattgefunden hat. / Scholars of Origen have for decades agreed in thinking that the relationship between him and the simple believers is characterised by reservations right up to hostility. Researching the plenty of pertinent statements in their context, the study reveals a differentiated view and detects an advancement in Origens understanding for the simple members of the Christian community.
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Über Vorsehung = Peri pronoias /Gregorius Schwab, Andreas. January 2009 (has links)
Überarb. Magisterarbeit, Universiẗat München. / Text griechisch und deutsch. Originaltitel: Peri pronoias.
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The suffering of the impassible God : the dialectics of patristic thought /Gavriljuk, Pavel L. January 2005 (has links)
Texas, Southern Methodist University, Diss., 2001--Dallas.
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John the Baptist Through the Lens of Generative AI : A Narrative and Reception-Historical Analysis of Mark 1Wettervik, Daniel January 2023 (has links)
This thesis addresses the intersection of reception history in biblical studies, Generative Artificial Intelligence (GAI) and phenomenology. Three images, from text prompts using different English translations of Mark 1:1–8 (KJV, NRSV and NIV) have been generated by GAI. In addition to the three translations, a more encompassing body of information, based on exegetical analysis, reception history and recent scholarly literature on John the Baptist and Mark 1, was also provided. Mark 1 is analyzed using narrative criticism with special focus on John the Baptist. Current research on the historical John is discussed, alongside interpretations of John from Late Ancient Christian Sources seen from a phenomenological perspective. Traditionally, interpreting biblical art and text has assumed an artist portraying a narrative reading using methods such as visual exegesis. With GAI, this has changed moving the artist from the canvas to the text prompt. It puts the biblical text in a direct causal connection to the created image. Previously the artist had to decide when the image was finished but with GAI the decision is about which image to keep. The purpose of the image becomes a focal point. Images created with this modern technology can be relevant in at least two regards. First, they do represent a new type of biblical art. Second, the iterative process itself is a novel approach to studying and interacting with the Bible. Challenges exists, such as a bias towards Western/American cultural, sociological, and economical values. Data scientists and mathematicians are determining the probabilistic models without problematizing the content. Ethical questions in this field need to be addressed. GAI learning from AI-produced data – instead of human data – will likely become an issue, thus reinforcing existing biases and prejudices further.
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