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Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi

Yıl 2025, Cilt: 26 Sayı: 3, 1099 - 1121, 28.11.2025

Öz

Bu çalışma, dijitalleşmeyle birlikte dönüşen bilgi üretim süreçlerini, hesaplamalı sosyal bilimlerin (HSB) ortaya koyduğu yeni epistemolojik yaklaşımlar bağlamında ele almaktadır. HSB, büyük veri setlerini işleyebilen yöntem ve algoritmalar aracılığıyla toplumsal olguların niceliksel ölçümüne imkân verirken, bilgi üretimini veri merkezli ve hesaplamalı bir düzleme taşımaktadır. Bu dönüşüm süreci, epistemolojik sınırlılıklar, normatif belirsizlikler, etik açmazlar ve metodolojik indirgemecilik gibi sorunları da beraberinde getirmektedir. Nitekim çalışma, hesaplamalı yöntemlerin sağladığı olanaklara da değinmekle birlikte, HSB’nin alternatif epistemolojisine eleştirel bir yerden de bakmaktadır. Hesaplamalı yöntemlerin sunduğu fırsatları yalnızca teknik avantajlar olarak değil, aynı zamanda sosyal bilimlerde alternatif bilgi rejimlerinin kurulması açısından da değerlendirmektedir. HSB’nin sunduğu dönüşüm, sosyal bilimlerdeki bilgi-iktidar ilişkilerini, politik konumlanışları, etik tartışmaları ve yöntemsel bulanıklıkları yeniden düşünmeyi gerekli kılmaktadır.

Kaynakça

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  • Elragal, A., & Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4, 1–20.
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The Epistemology of Digital Knowledge: The Impact of Computational Social Sciences on Knowledge Production

Yıl 2025, Cilt: 26 Sayı: 3, 1099 - 1121, 28.11.2025

Öz

This study examines the transformation of knowledge production processes in the context of digitalization, focusing on the new epistemological approaches introduced by computational social sciences (CSS). This shift marks the rise of new knowledge forms based on correlations, patterns, and predictions—departing from traditional positivist epistemology. However, this process also brings epistemological limitations, normative ambiguities, ethical dilemmas, and methodological reductionism. While addressing the potentials offered by computational methods, the study also adopts a critical stance toward CSS’s alternative epistemology. It considers the opportunities of computational approaches not merely as technical advantages, but as pathways for constructing alternative knowledge regimes within the social sciences. The transformation driven by CSS necessitates a reconsideration of knowledge-power relations, political orientations, ethical debates, and methodological ambiguities in contemporary social research.

Kaynakça

  • Abdelrahman, M., & Wang, Q. (2020). A review of deep learning approaches for automatic classroom engagement recognition. Computers & Education, 147.
  • Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine, 16(7), 16–07.
  • Ashley, K. D. (2017). Artificial intelligence and legal analytics. Cambridge University Press.
  • Avnoon, N., Kotliar, D. M., & Rivnai-Bahir, S. (2024). Contextualizing the ethics of algorithms: A socio-professional approach. New Media & Society, 26(10), 5962–5982.
  • Beer, D. (2019). The data gaze: Capitalism, power and perception. SAGE Publications Ltd.
  • Bergman, M. M., & Jean, N. (2021). Big data and the illusion of choice. Social Science Computer Review, 39(5), 866–880. https://doi.org/10.1177/0894439320936336
  • Blevins, C., & Mullen, L. (2015). Jane, John... Leslie? A historical method for algorithmic gender prediction. Digital Humanities Quarterly, 9(3). http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html
  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7280–7287. https://doi.org/10.1073/pnas.082080899
  • Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895. https://doi.org/10.1126/science.1165821
  • Bowker, G. C., & Gitelman, L. (Eds.). (2013). “Raw data” is an oxymoron. MIT Press.
  • boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
  • Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512
  • Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Oxford University Press.
  • Cantekin, C. (2024). Değişken şartlar altında atıksu arıtımında karbon ve besi maddesi gideriminin makine öğrenmesi yöntemleri ile modellenmesi (Yayımlanmamış doktora tezi). Maltepe Üniversitesi, Türkiye.
  • Coleman, J. S. (1965). The use of electronic computers in the study of social organisation. European Journal of Sociology / Archives Européennes de Sociologie, 6(1), 89–107.
  • Conte, R., & Paolucci, M. (2014). On agent-based modeling and computational social science. Frontiers in Psychology, 5, 668.
  • Darian, S., Chauhan, A., Marton, R., Ruppert, J., Anderson, K., Clune, R., ... & Voida, A. (2023). Enacting data feminism in advocacy data work. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1–28.
  • Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. https://doi.org/10.1145/2347736.2347755
  • Durán, J. M., & Formanek, N. (2018). Grounds for trust: Essential epistemic opacity and computational reliabilism. Minds and Machines, 28(4), 645–666. https://doi.org/10.1007/s11023-018-9481-6
  • Elragal, A., & Klischewski, R. (2017). Theory-driven or process-driven prediction? Epistemological challenges of big data analytics. Journal of Big Data, 4, 1–20.
  • Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.
  • Eşidir, K. A. (2025). Makine öğrenimi modelleri ile yetişkin eğitimi analizi: Modellerin karşılaştırmalı performansı. Elektronik Sosyal Bilimler Dergisi, 24(2), 946–964.
  • Floridi, L. (2021). The logic of information: A theory of philosophy as conceptual design. Oxford University Press.
  • Füller, H. (2018). The politics of datafication: Algorithmic surveillance and the public sphere. Media, Culture & Society, 40(5), 745–758.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  • Gray, J., & Witt, A. (2021). A feminist data ethics of care for machine learning: The what, why, who and how. First Monday, 26(6). https://doi.org/10.5210/fm.v26i6.11071
  • Grimmer, J., & Stewart, B. M. (2021). Machine learning for social science: An agnostic approach. Annual Review of Political Science, 24, 395–419.
  • Grondin, D., & Hogue, S. (2024). Person of interest as media technology of surveillance: A cautionary tale for the future of the national security state with diegetic big data surveillance, algorithmic security, and artificial intelligence. Television & New Media, 25(4), 334–351.
  • Grondin, D., & Hogue, S. (2024). Person of interest as media technology of surveillance: A cautionary tale for the future of the national security state with diegetic big data surveillance, algorithmic security, and artificial intelligence. Television & New Media, 25(4), 334-351.
  • Guetzkow, H. (1959). A use of simulation in the study of inter‐nation relations. Behavioral Science, 4(3), 183–191. Guggenheim, L., Jang, S. M., Bae, S. Y., & Neuman, W. R. (2015). The dynamics of issue frame competition in traditional and social media. The ANNALS of the American Academy of Political and Social Science, 659(1), 207–224.
  • Guggenheim, L., Jang, S. M., Bae, S. Y., & Neuman, W. R. (2015). The dynamics of issue frame competition in traditional and social media. The ANNALS of the American Academy of Political and Social Science, 659(1), 207-224.
  • Hagenaars, J. A., & McCutcheon, A. L. (Eds.). (2002). Applied latent class analysis. Cambridge University Press. Hannák, A., Wagner, C., Garcia, D., Mislove, A., Strohmaier, M., & Wilson, C. (2013). Bias in online freelance marketplaces: Evidence from TaskRabbit and Fiverr. In Proceedings of the 22nd International Conference on World Wide Web (pp. 527–538). ACM.
  • Hansen, J. T. (2004). Thoughts on knowing: Epistemic implications of counseling practice. Journal of Counseling & Development, 82(2), 131–138.
  • Holme, P., & Liljeros, F. (2015). Mechanistic models in computational social science. Frontiers in Physics, 3, 78.
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Toplam 97 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi ve Bilim Sosyolojisi, Bilim ve Teknoloji Sosyolojisi ve Sosyal Bilimler
Bölüm Derleme
Yazarlar

Emre Özcan 0000-0002-0877-2457

Beyza Yılmaz 0000-0002-6963-2036

Gönderilme Tarihi 25 Temmuz 2025
Kabul Tarihi 30 Ekim 2025
Yayımlanma Tarihi 28 Kasım 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 26 Sayı: 3

Kaynak Göster

APA Özcan, E., & Yılmaz, B. (2025). Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 26(3), 1099-1121.
AMA Özcan E, Yılmaz B. Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi. Kasım 2025;26(3):1099-1121.
Chicago Özcan, Emre, ve Beyza Yılmaz. “Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi”. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi 26, sy. 3 (Kasım 2025): 1099-1121.
EndNote Özcan E, Yılmaz B (01 Kasım 2025) Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi 26 3 1099–1121.
IEEE E. Özcan ve B. Yılmaz, “Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi”, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, c. 26, sy. 3, ss. 1099–1121, 2025.
ISNAD Özcan, Emre - Yılmaz, Beyza. “Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi”. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi 26/3 (Kasım2025), 1099-1121.
JAMA Özcan E, Yılmaz B. Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi. 2025;26:1099–1121.
MLA Özcan, Emre ve Beyza Yılmaz. “Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi”. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, c. 26, sy. 3, 2025, ss. 1099-21.
Vancouver Özcan E, Yılmaz B. Dijital Bilginin Epistemolojisi: Hesaplamalı Sosyal Bilimlerin Bilgi Üretimi Üzerine Etkisi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi. 2025;26(3):1099-121.