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How do signals of academic performance vary across disciplines? Evidence from a survey experiment with university professors in Germany.

Petzold, K., & Netz, N. (2023).
How do signals of academic performance vary across disciplines? Evidence from a survey experiment with university professors in Germany. Soziale Welt, 26, 490-523. https://doi.org/10.5771/9783748925590-490
Abstract

While recent research has investigated what signals of academic performance govern academics’ access to professorships, whether the power of such signals varies across disciplines has to date hardly been examined. We argue that the signaling power of academic achievements depends on the discipline-specific degree of standardization of research and on the spatio-temporal universality of research objects. Using a factorial survey experiment with Germany-based university professors of German studies, selected social sciences, and chemistry, we investigate the suitability of fictitious candidates for a tenured professorship (N respondents = 874, N vignettes = 6354).

Laughing out loud – Exploring AI-generated and human-generated humor.

Safikhani, P., Avetisyan, H., & Broneske, D. (2023).
Laughing out loud – Exploring AI-generated and human-generated humor. Computer Science & Information Technology (CS & IT), 2023, 59-76.

Mustervertrag Datennutzung KonsortSWD (Version 3.0.0).

Schallaböck, J., Hoffstätter, U., Buck, D., & Linne, M. (2023).
Mustervertrag Datennutzung KonsortSWD (Version 3.0.0). Hannover: DZHW. https://doi.org/10.5281/zenodo.10409864

Mustervertrag Datenaufnahme KonsortSWD (Version 2.0.0).

Schallaböck, J., Kreutzer, T., Hoffstätter, U., & Buck, D. (2023).
Mustervertrag Datenaufnahme KonsortSWD (Version 2.0.0). Hannover: DZHW. https://doi.org/10.5281/zenodo.10406480

Who Benefits from Job Security? Job Satisfaction and Performance in Academia.

Ambrasat, J., & Fabian, G. (2023).
Who Benefits from Job Security? Job Satisfaction and Performance in Academia. SocArXiv. https://doi.org/10.31235/osf.io/7j2pt (Abgerufen am: 20.12.2023). https://doi.org/10.31235/osf.io/7j2pt

Internationalisation in the digital transformation: A scoping review.

Ferreira Santos, L. (2023).
Internationalisation in the digital transformation: A scoping review. Higher Education Quarterly, 2023, 1-18 (online first). https://doi.org/10.1111/hequ.12488
Abstract

This scoping review aims to trace the evidence in the international scientific literature on the intersections between COVID-driven digitalisation and the internationalisation strategies of higher education institutions (HEIs). The focus lies on teaching and learning, cooperation, support structures, and leadership processes. We reviewed 45 studies from an initial selection of 648 sources retrieved from Scopus and Web of Science. Results showed that digitally supported internationalisation strategies are often associated with more cost-effective, inclusive and sustainable models. Despite ongoing calls for comprehensive approaches, the systematic adoption of virtual internationalisation strategies remains largely a normative subject.

The Many Roads to Transparency: A Typology of Topics and Varieties in the Transparency Literature.

Cruz Romero, R. (2023).
The Many Roads to Transparency: A Typology of Topics and Varieties in the Transparency Literature. Revista Española de la Transparencia, 18(3), 293-329. https://doi.org/10.51915/ret.286

Ordering the past, envisioning future(s): how review articles in synthetic biology make use of heterogeneous expectations.

Blümel, C. (2023).
Ordering the past, envisioning future(s): how review articles in synthetic biology make use of heterogeneous expectations. Futures (online first). https://doi.org/10.1016/j.futures.2023.103302
Abstract

This article deals with expectation dynamics in the field of synthetic biology. The article draws on scholarly review articles as the main material, complemented by expert interviews conducted with scholars from the field. The aim is to explore how expectations change over time and how they are used to justify and move the field. Drawing from conceptual advances of the sociology of expectations, I show how expectations are increasingly linked at different levels (the landscape, sector, and niche level) and how they support and justify the field among different audiences.

Wissenschaftssoziologie. Literaturbesprechung.

Hesselmann, F. (2023).
Wissenschaftssoziologie. Literaturbesprechung. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 75(4) (online first). https://doi.org/10.1007/s11577-023-00926-y

Anomaly detection algorithms: Comparative analysis and explainability perspectives.

Darab, S., Allipilli, H., Ghani, S., Changaramkulath, H., Koneru, S., Broneske, D., & Saake, G. (2023).
Anomaly detection algorithms: Comparative analysis and explainability perspectives. In D. Benavides-Prado et al. (Hrsg.), Data Science and Machine Learning, 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings (S. 90-104). Singapore: Springer Nature.

The Long and Winding Road to Excellence: The German Case.

Möller, T., & Hornbostel, S. (2023).
The Long and Winding Road to Excellence: The German Case. In M. Yudkevich, P. G. Altbach, & J. Salmi (Hrsg.), Academic Star Wars: Excellence Initiatives in Global Perspective (S. 177-201). Cambridge, Massachusetts: The MIT Press. https://doi.org/10.7551/mitpress/14601.003.0012

Dropout intent of students with disabilities.

Rußmann, M., Netz, N., & Lörz, M. (2023).
Dropout intent of students with disabilities. Higher Education (online first). https://doi.org/10.1007/s10734-023-01111-y
Abstract

We examine the mechanisms explaining the dropout intentions of students with disabilities by integrating Tinto’s model of student integration, the student attrition model, the composite persistence model, and insights from social stratification research. Overall, our results highlight the importance of considering both students’ integration into higher education and their private resources for understanding student-group-specific dropout intent.

The sound of respondents: predicting respondents’ level of interest in questions with voice data in smartphone surveys.

Höhne, J. K., Kern, C., Gavras, K., & Schlosser, S. (2023).
The sound of respondents: predicting respondents’ level of interest in questions with voice data in smartphone surveys. Quality & Quantity, International Journal of Methodology, 57(6). https://doi.org/10.1007/s11135-023-01776-8
Abstract

Web surveys completed on smartphones open novel ways for measuring respondents’ attitudes, behaviors, and beliefs that are crucial for social science research and many adjacent research fields. In this study, we make use of the built-in microphones of smartphones to record voice answers in a smartphone survey and extract non-verbal cues, such as amplitudes and pitches, from the collected voice data. This allows us to predict respondents’ level of interest (i.e., disinterest, neutral, and high interest) based on their voice answers, which expands the opportunities for researching respondents’ engagement and answer behavior. [...] Full abstract: https://doi.org/10.1007/s11135-023-01776-8

Spezifische Bedarfe von wissenschaftlich Tätigen aus dem Ausland sollten stärker berücksichtigt werden.

Jaudzims, S., & Oberschelp, A. (28. November 2023).
Spezifische Bedarfe von wissenschaftlich Tätigen aus dem Ausland sollten stärker berücksichtigt werden [Blogbeitrag]. Abgerufen von https://www.wissenschaft-weltoffen.de/de/2023/11/28/spezifische-bedarfe-von-wissenschaftlich-taetigen-aus-dem-ausland-sollten-staerker-beruecksichtigt-werden/

Data Mesh for Managing Complex Big Data Landscapes and Enhancing Decision Making in Organizations.

Azeroual, O., & Nacheva, R. (2023).
Data Mesh for Managing Complex Big Data Landscapes and Enhancing Decision Making in Organizations. In L. Gruenwald, E. Masciari, C. Rolland, & J. Bernardino (Hrsg.), Proceedings of the 15th International Conference on Knowledge Management and Information Systems (KMIS 2023) (S. 202-212). Rome, Italy: SciTePress, Science and Technology Publications, Lda. https://doi.org/10.5220/0012195700003598

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