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To facilitate the use of relevant measures that can be used for contrastive text analysis, we are developing a tool written in Python called pydistinto as part of the Zeta and Company project. The goal of our project is to achieve a deeper understanding of the different measures of distinctiveness and to propose improvements for their implementation and use. For this purpose, we created a conceptual framework of different measures of distinctiveness. Based on the framework and Codes from pyzeta, pydistinto has been developed. DFG Schwerpunktprogramm SPP 2207 "Computational Literary Studies" Online: https://gepris.dfg.de/gepris/projekt/402743989 https://dfg-spp-cls.github.io/ Teilprojekt: "Zeta und Konsorten. Distinktivitätsmaße für die Digitalen Literaturwissenschaften" Online: https://gepris.dfg.de/gepris/projekt/424211690 https://dfg-spp-cls.github.io/projects_en/2020/01/24/TP-Zeta_and_Company/ https://zeta-project.eu/de/
If you use this software, please cite it as below.
measure of distinctiveness, Computational Literary Studies, SPP 2207
measure of distinctiveness, Computational Literary Studies, SPP 2207
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 17 | |
| downloads | 1 |

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