56 research outputs found
Zum Mechanismus massenspektrometrischer Fragmentierungsreaktionen. VI. Äthylen-Eliminierung aus Molekül-Ionen des Tetralins
Get PDFGrützmacher H-F, Puschmann M. Zum Mechanismus massenspektrometrischer Fragmentierungsreaktionen. VI. Äthylen-Eliminierung aus Molekül-Ionen des Tetralins. Chemische Berichte. 1970;104(7):2079-2089
Female Artist Figures in Margaret Atwood's Alias Grace and The Blind Assassin
No full textThe author is dead; long live the author. In a time when discourses, words and structures determine the discussion about literary texts, paradoxically, the figure of the artist looms large in novels, short stories, movies and plays. In a “post-Barthesian age” (Scherzinger) the figure of the artist is ascribed more and more significance. While the portraits of the artist as a young man are well-researched and documented, female artist figures in literature(s) in English are still more or less neglected. This volume of anglistik & englischunterricht attempts to fill the gap. The focus of the essays lies, firstly, on the (de-)constructions of gender, secondly, the complex self-reflexive functions of the artist figures and, thirdly, on the negotiations of cultural faultlines
Female Artist Figures in Margaret Atwood's Alias Grace and The Blind Assassin
No full textThe author is dead; long live the author. In a time when discourses, words and structures determine the discussion about literary texts, paradoxically, the figure of the artist looms large in novels, short stories, movies and plays. In a “post-Barthesian age” (Scherzinger) the figure of the artist is ascribed more and more significance. While the portraits of the artist as a young man are well-researched and documented, female artist figures in literature(s) in English are still more or less neglected. This volume of anglistik & englischunterricht attempts to fill the gap. The focus of the essays lies, firstly, on the (de-)constructions of gender, secondly, the complex self-reflexive functions of the artist figures and, thirdly, on the negotiations of cultural faultlines
State and Law
Get PDFThis is the author accepted manuscript. The final version is available in print format only from Bloombsbury
Risk Analysis (Assessment) Using Virtual Reality Technology - Effects of Subjective Experience: An Experimental Study
No full textAbstractDepending on the specific design phase and relevant goals, engineers have various options to visualize machine tool development. This study examined two types of visualization (e.g. concerning complexity, colors, animations, vividness) using VR technology. Over 25 experts were asked to identify and assess hazards in two 3D-models that differed in complexity. Besides technical aspects, we tested whether psychological aspects such as sense of “being there” and the quality of the risk assessment were affected by the type of the 3D-representation. Furthermore the relations between the user‘s traits (e.g. conscientiousness, risk perception, etc.) and the properties of the 3D-models were explored
Unique Biradical Intermediate in the Mechanism of the Heme Enzyme Chlorite Dismutase
No full textThe heme enzyme chlorite dismutase (Cld) catalyzes O-O bond formation as part of the conversion of the toxic chlorite (ClO2-) to chloride (Cl-) and molecular oxygen (O2). Enzymatic O-O bond formation is rare in nature, and therefore, the reaction mechanism of Cld is of great interest. Microsecond timescale pre-steady-state kinetic experiments employing Cld from Azospira oryzae (AoCld), the natural substrate chlorite, and the model substrate peracetic acid (PAA) reveal the formation of distinct intermediates. AoCld forms a complex with PAA rapidly, which is cleaved heterolytically to yield Compound I, which is sequentially converted to Compound II. In the presence of chlorite, AoCld forms an initial intermediate with spectroscopic characteristics of a 6-coordinate high-spin ferric substrate adduct, which subsequently transforms at kobs = 2-5 × 104 s-1 to an intermediate 5-coordinated high-spin ferric species. Microsecond-Timescale freeze-hyperquench experiments uncovered the presence of a transient low-spin ferric species and a triplet species attributed to two weakly coupled amino acid cation radicals. The intermediates of the chlorite reaction were not observed with the model substrate PAA. These findings demonstrate the nature of physiologically relevant catalytic intermediates and show that the commonly used model substrate may not behave as expected, which demands a revision of the currently proposed mechanism of Clds. The transient triplet-state biradical species that we designate as Compound T is, to the best of our knowledge, unique in heme enzymology. The results highlight electron paramagnetic resonance spectroscopic evidence for transient intermediate formation during the reaction of AoCld with its natural substrate chlorite. In the proposed mechanism, the heme iron remains ferric throughout the catalytic cycle, which may minimize the heme moiety's reorganization and thereby maximize the enzyme's catalytic efficiency. BT/Biocatalysi
Actors (Automated Content Analysis)
Get PDFActors in coverage might be individuals, groups, or organizations, which are discussed, described, or quoted in the news.
The datasets referred to in the table are described in the following paragraph:
Benoit and Matuso (2020) uses fictional sentences (N = 5) to demonstrate how named entities and noun phrases can be identified automatically. Lind and Meltzer (2020) demonstrate the use of organic dictionaries to identify actors in German newspaper articles (2013-2017, N = 348,785). Puschmann (2019) uses four data sets to demonstrate how sentiment/tone may be analyzed by the computer. Using tweets (2016, N = 18,826), German newspaper articles (2011-2016, N = 377), Swiss newspaper articles (2007-2012, N = 21,280), and debate transcripts (1970-2017, N = 7,897), he extracts nouns and named entities from text. Lastly, Wiedemann and Niekler (2017) extract proper nouns from State of the Union speeches (1790-2017, N = 233).
Field of application/theoretical foundation:
Related to theories of “Agenda Setting” and “Framing”, analyses might want to know how much weight is given to a specific actor, how these actors are evaluated and what perspectives and frames they might bring into the discussion how prominently.
References/combination with other methods of data collection:
Oftentimes, studies use both manual and automated content analysis to identify actors in text. This might be a useful tool to extend the lists of actors that can be found as well as to validate automated analyses. For example, Lind and Meltzer (2020) combine manual coding and dictionaries to identify the salience of women in the news.
Table 1. Measurement of “Actors” using automated content analysis.
Author(s)
Sample
Procedure
Formal validity check with manual coding as benchmark*
Code
Benoit & Matuso (2020)
Fictional sentences
Part-of-Speech tagging; syntactic parsing
Not reported
https://cran.r-project.org/web/packages/spacyr/vignettes/using_spacyr.html
Lind & Meltzer
(2020)
Newspapers
Dictionary approach
Reported
https://osf.io/yqbcj/?view_only=369e2004172b43bb91a39b536970e50b
Puschmann (2019)
(a) Tweets
(b) German newspaper articles
(c) Swiss newspaper articles
(d) United Nations General Debate Transcripts
Part-of-Speech tagging; syntactic parsing
Not reported
http://inhaltsanalyse-mit-r.de/ner.html
Wiedemann & Niekler (2017)
State of the Union speeches
Part-of-Speech tagging
Not reported
https://tm4ss.github.io/docs/Tutorial_8_NER_POS.html
*Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results.
References
Benoit, K., & Matuso. (2020). A Guide to Using spacyr. Retrieved from https://cran.r-project.org/web/packages/spacyr/vignettes/using_spacyr.html
Lind, F., & Meltzer, C. E. (2020). Now you see me, now you don’t: Applying automated content analysis to track migrant women’s salience in German news. Feminist Media Studies, 1–18.
Puschmann, C. (2019). Automatisierte Inhaltsanalyse mit R. Retrieved from http://inhaltsanalyse-mit-r.de/index.html
Wiedemann, G., Niekler, A. (2017). Hands-on: a five day text mining course for humanists and social scientists in R. Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017), Berlin. Retrieved from https://tm4ss.github.io/docs/index.htm
Topics (Automated Content Analysis)
Get PDFTopics describe the main issue discussed in an article, for example: Does an article deal with politics, economics or sports?
Field of application/theoretical foundation:
In the context of “Agenda Setting”, studies analyze which issues are on the public agenda. In the context of “News Values”, studies may analyze why some topics are more prominently covered than others.
References/combination with other methods of data collection:
Many studies combine manual inspection of topics with their automated detection. Quinn et al. (2010) demonstrate for their analyses of legislative speeches how manual inspection may increase the validity of results. Similarly, Hase et al. (2020) use automated content analysis to find and map similar topics for which manual coding is then conducted. Such combinations may contribute to a better and more detailed understanding of topics than automated analyses by themselves.
The datasets referred to in the table are described in the following paragraph:
Puschmann (2019a) uses New York Times articles (1996-2006, N = 30,862) as well as articles from Die Zeit (2011-2016, N = 377) to identify topics using supervised machine learning. In another tutorial, Puschmann (2019b) uses Sherlock Holmes stories (18th century, N = 12), articles from Die Zeit (2011-2016, N = 377) and debate transcripts (1970-2017, N = 7,897) to apply LDA and structural topic modeling. In her tutorials, Silge (2018a, 2018b) also uses Sherlock Holmes stories (18th century, N = 12) and a news corpus also containing comments (2006-ongoing, N = 100,000). Silge and Robinson (2020) apply LDA topic modeling on news stories by the Associated Press (1992, N = 2,246) as well as books by Dickens, Wells, Verne and Austen (18th century, N = 4). Roberts et al. (2019) use blogposts (2008, N = 13,248) for structural topic modeling. Watanabe and Müller (2019) apply LDA topic modeling on newspaper articles from The Guardian (2016, N = 6,000). Van Atteveldt and Welbers (2019, 2020) use State of the Union speeches (1981-2017, N = 10 and 1789-2017, N = 58) for their analyses. Lastly, Wiedemann and Niekler (2017) use the same data containing State of the Union speeches (1790-2017, N = 223).
Table 1. Measurement of “Topics” using automated content analysis.
Author(s)
Sample
Procedure
Formal validity check with manual coding as benchmark*
Code
Puschmann (2019a)
(a) Newspaper articles
(b) Newspaper articles
Supervised machine learning
Reported
http://inhaltsanalyse-mit-r.de/maschinelles_lernen.html
Puschmann (2019b)
(a) Sherlock Holmes stories
(b) Newspaper articles
(c) United Nations General Debate Transcripts
LDA topic modeling; structural topic modeling
Not reported
http://inhaltsanalyse-mit-r.de/themenmodelle.html
Silge (2018a) & Silge (2018b)
(a) Sherlock Holmes stories
(b) News stories and comments
t
Structural topic modeling
Not reported
https://juliasilge.com/blog/sherlock-holmes-stm/ & https://juliasilge.com/blog/evaluating-stm/
Silge & Robinson
(2020)
(a) News articles
(b) Books
LDA topic modeling
Not reported
https://www.tidytextmining.com/topicmodeling.html
Roberts et al.
(2019)
Blogposts
Structural topic modeling
Not reported
https://www.jstatsoft.org/article/view/v091i02
Watanabe & Müller
(2019)
Newspaper articles
LDA topic modeling
Not reported
https://tutorials.quanteda.io/machine-learning/topicmodel/
van Atteveldt & Welbers
(2019)
State of the Union speeches
Structural topic modeling
Not reported
https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_stm.md
van Atteveldt & Welbers
(2020)
State of the Union speeches
LDA topic modeling
Not reported
https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_lda.md
Wiedemann & Niekler (2017)
State of the Union speeches
LDA topic modeling
Not reported
https://tm4ss.github.io/docs/Tutorial_6_Topic_Models.html
Wiedemann & Niekler (2017)
State of the Union speeches
Supervised machine learning
Reported
https://tm4ss.github.io/docs/Tutorial_7_Klassifikation.html
*Please note that many of the sources listed here are tutorials on how to conducted automated analyses – and therefore not focused on the validation of results. Readers should simply read this column as an indication in terms of which sources they can refer to if they are interested in the validation of results.
References
Hase, V., Engelke, K., Kieslich, K. (2020). The things we fear. Combining automated and manual content analysis to uncover themes, topics and threats in fear-related news. Journalism Studies, 21(10), 1384-1402.
Puschmann, C. (2019). Automatisierte Inhaltsanalyse mit R. Retrieved from http://inhaltsanalyse-mit-r.de/index.html
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), 209–228.
Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm: An R Package for Structural Topic Model. Journal of Statistical Software, 91(2), 1–40.
Silge, J. (2018a). The game is afoot! Topic modeling of Sherlock Holmes stories. Retrieved from https://juliasilge.com/blog/sherlock-holmes-stm/
Silge, J. (2018b). Training, evaluating, and interpreting topic models. Retrieved from https://juliasilge.com/blog/evaluating-stm/
Silge, J., & Robinson, D. (2020). Text Mining with R. A tidy approach. Retrieved from https://www.tidytextmining.com/
van Atteveldt, W., & Welbers, K. (2019). Structural Topic Modeling. Retrieved from https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_stm.md
van Atteveldt, W., & Welbers, K. (2020). Fitting LDA models in R. Retrieved from https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/r_text_lda.md
Watanabe, K., & Müller, S. (2019). Quanteda tutorials. Retrieved from https://tutorials.quanteda.io/
Wiedemann, G., Niekler, A. (2017). Hands-on: a five day text mining course for humanists and social scientists in R. Proceedings of the 1st Workshop Teaching NLP for Digital Humanities (Teach4DH@GSCL 2017), Berlin. Retrieved from https://tm4ss.github.io/docs/index.htm
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