Analysis of the Role of Citizen Science in the Formation and Development of Classic Persian Literature Linguistic Corpora

Document Type : Original Article

Authors

1 Tehran University, Tehran, Iran.

2 .Associate professor, Department of Knowledge and Information Science, Tehran University, Tehran, Iran. Iran.

10.22126/tbih.2024.9567.1000

Abstract

Objective: Given its official status in Iran and three other countries, Persian ranks as the ninth most frequently used language in terms of web content and substance, surpassing Arabic, Turkish, and other Middle Eastern languages. Consequently, Persian language processing has become a national and international necessity. This research investigates the role of citizen science in the formation and development of language corpora for classical Persian literature. The goal of this study is to explore how public participation can be used to create and enrich language corpora for classical Persian literature.
Method: This study begins with a qualitative approach using a seven-step meta-synthesis method developed by Sandelowski and Barroso to examine corpus components, characteristics, and applications. Following a literature review, a system was designed and the first indigenous citizen science platform for classical Persian literature was implemented.
Findings: The findings indicated that codes such as citizen science, machine learning, deep learning, data, information, cyberspace, and others received significant attention in the reviewed articles. Additionally, 15 other codes with the highest frequency were extracted from these articles, which led to the design of a system for the indigenous citizen science platform.
Conclusion: The results of user interaction with this platform demonstrate that citizen science can be a valuable and effective tool for promoting classical Persian literature in the digital world. This tool can help to increase the volume and diversity of corpus data, improve data accuracy and quality, reduce data collection and processing costs, and increase public commitment and participation in the preservation and promotion of classical Persian literature

Keywords


Ahumada, J. A., Fegraus, E., Birch, T., Fores, N., Kays, R., O’Brien, T. G., et al. (2020). Wildlife insights: A platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environmental Conservation, 47(1).
Ceccaroni, L., Bibby, J., Roger, E., Flemons, P., Michael, K., Fagan, L., & Oliver, J. L. (2019). Opportunities and risks for citizen science in the age of artificial intelligence. Citizen Science: Theory and Practice, 4(1), 29.
Meurers, D. (2015). Learner corpora and natural language processing. The Cambridge handbook of learner corpus research, 537-566.
Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2019). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. In T. Bui (Ed.), Proceedings of the Hawaii International Conference on System Sciences (HICSS). (3):15-19
Flage, A. (2024). Taking games: a meta-analysis. Journal of the Economic Science Association, 1-24.
Hamatt, D, Staeheli, C (2011). Respect and responsibility: Teaching citizenship in South African high schools International. Journal of Educational Development, 31 (3). p:14-27
Hand, E. (2010). "Citizen science: People power". Nature. 466 (7307): 685–687.
Kennedy, Graeme (1998). An Introduction to CorPus Linguistics. London: Longman. 13-85.
Lehejcek, J., Adam, M., Tomasek, P., & Trojan, J. (2019). Informacni system pro spravu fotopasti (National database of photo trap records).
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.
Peterson, Andrew & Knowles, Catherine (2009). Active Citizenship: A Preliminary Study into Student Teacher Understandings. Journal of Educational Research, Vol 51 No 1, PP 39-59.
Sandelowski, M., Docherty, S., & Emden, C. (1997). Focus on qualitative methods Qualitative metasynthesis: issues and techniques. Research in nursing and health, 20, 365-372
Swanson, A., Kosmala, M., Lintott, C., & Packer, C. (2016). A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conservation Biology, 30 (3), 520–531.
Purta, J (2018). Civic Education, In: International Encycloped Curriculum. Dergamon press, v (9):117-132
Trojan, J., Schade, S., Lemmens, R., & Frantál, B. (2019). Citizen science as a new approach in geography and beyond: Review and reflections. Moravian Geographical Reports, 27(4), 254–264.
Yick, Alice G. (2013), “A Meta synthesis of Qualitative Findings on the Role of Spirituality and Religiosity Among Culturally Diverse Domestic Violence Survivors”. Health Policy & Services, 37 out of 70.
Zimmer L. (2006), “Qualitative meta-synthesis: a question of dialoguing with texts”, Journal of Advanced Nursing. 53(3): 311-318.
Sadeghi, S. S., Khotanlou, H., & Rasekh Mahand, M. (2021). Automatic Persian text emotion detection using cognitive linguistic and deep learning. Journal of AI and Data Mining, 9(2), 169-179.
Urválková, E. S., & Janoušková, S. (2019). Citizen science–bridging the gap between scientists and amateurs. Chemistry Teacher International, 1(2), 20180032
Yang, D., Wan, H. Y., Huang, T. K., & Liu, J. (2019). The role of citizen science in conservation under the telecoupling framework. Sustainability, 11(4), 1108.