Analisi del data turn nella filosofia del linguaggio nell'era dei big data

Autori

Parole chiave:

Filosofia del linguaggio, Big data, Data turn, Analisi quantitative

Abstract

La raccolta di dati nella nostra era dell'”Information Technology” ha generato una rivoluzione nella conoscenza. Nell'era dei “big data”, la conseguente crescita senza precedenti dei dati, ha reso necessari cambiamenti nella scala, nella natura e nello stato dei dati, portando quindi i ricercatori ad adottare nuovi paradigmi e metodologie nella ricerca filosofica. In particolare, l'attenzione teorica della filosofia del linguaggio si è spostata verso la conoscenza cognitiva, con un'enfasi sulla proposizione particolare del “data turn” nella cognizione cognitiva nell'era dei “big data”. Il paper esplora la potenziale portata della ricerca quantitativa del “data turn” nella filosofia del linguaggio, tramite l’analisi della necessità di trasformare i paradigmi della ricerca qualitativa e quantitativa, ricostruendo l'approccio quantitativo della filosofia del linguaggio ed ampliando l’analisi delle relazioni uomo-dati nella filosofia dei “big data”. Il paper conclude affermando che sono necessarie ulteriori ricerche per esaminare in modo ancor più approfondito la relazione tra linguaggio, dati e filosofia.

Biografie autore

  • Shasha Xu, Zhejiang University of Finance & Economics

    Ph. D. Associate Professor. School of Foreign Languages, Zhejiang University of Finance & Economics, Hangzhou, 310018 – China. Orcid: https://orcid.org/0000-0003-0597-3517.

  • Qian Yang, Zhejiang University of Finance & Economics

    School of Foreign Languages, Zhejiang University of Finance & Economics, Hangzhou, 310018 – China. Orcid: https://orcid.org/0000-0001-5422-7022.

Riferimenti bibliografici

AGERRI, R.; ARTOLA, X.; BELOKI, Z. et al. Big Data for Natural Language Processing: A Streaming Approach. Knowledge-Based Systems, v. 79, n. 5, p. 36-42, 2015.

ALBALADEJO, T. Retórica Cultural, Lenguaje Retórico Y Lenguaje Literario. Tonos Digital, v. 25, p. 1-21, 2013.

ARPPE, A.; JARVIKIVI, J. Every Method Counts: Combining Corpus-based and Experimental Evidence in the Study of Synonymy. Corpus Linguistics and Linguistic Theory, v. 3, n. 2, p. 131-159, 2007.

AUSTIN, J. L. How to Do Things with Words. Oxford: The Oxford University Press, 1962.

AYER, A. J. Language, Truth and Logic. London: Victor Gollancz, 1946.

BAKER, M. C. The Polysynthesis Parameter. Oxford: Oxford University Press, 1996.

BASDEN, A.; KLEIN, H. K. New Research Directions for Data and Knowledge Engineering: A Philosophy of Language Approach. Data & Knowledge Engineering, v. 67, n. 2, p. 260-285, 2008.

BOGEN, J. Noise in the World. Philosophy of Science, v. 77, n. 5, p. 778-791, 2010.

BOYD, D.; CRAWFORD, K. Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information, Communication & Society, v. 15, n. 5, p. 662-679, 2012.

BURNET, J. Greek Philosophy: Thales to Plato. London: Macmillan, 1914.

CAI, L.; ZHU, Y. The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, v. 14, n. 2, p. 1-10, 2015.

CALUDE, C.S.; LONGÒ, G. The Deluge of Spurious Correlations in Big Data. Foundations of Science, v. 22, n. 3, p. 595-612, 2017.

CASSIRER, E. The Philosophy of Symbolic Forms: Volume 4 - The Metaphysics of Symbolic Forms. New Haven: Yale University Press, 1996.

COSTA, A. da. The Pardoner’s Passing and How It Matters Gender, Relics and Speech Acts. Critical Survey v. 29, n. 3, p. 27-47, 2017.

DE REGT, H. W. Understanding Scientific Understanding. Oxford: Oxford University Press, 2017.

DERRIDA, J. The Supplement of Copula: Philosophy before Linguistics. The Georgia Review, v. 30, n. 3, p. 527-564, 1976.

DEVITT, M. Testing Theories of Reference. In: HAUKIOJA, J. (ed.). Advances in Experimental Philosophy of Language. London & New York: Bloomsburry, 2015.

DUMMETT, M. Origins of Analytical Philosophy. Bloomsbury: Bloomsbury Academic, 2014.

ERL, T.; KHATTAK, W.; BULLER, P. Big Data Fundamentals: Concepts, Drivers and Techniques. Boston: Prentice Hall, 2016.

FLORIDI, L.; ILLARI, P. The Philosophy of Information Quality. Cham: Springer International, 2014.

FLORIDI, L. The Philosophy of Information. Oxford: Oxford University Press, 2011.

FREGE, G. Über Sinn und Bedeutung. Zeitschrift für Philosophie und philosophische Kritik, v. 100, p. 25-50, 1892.

FURNER, J. Philosophy of Data: Why? Education for Information, v. 33, n. 1, p. 55-70, 2017.

GAWDE, S.; PATIL, S.; KUMAR, S. et al. Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research. Engineering Applications of Artificial Intelligence. v. 123, Part A, August 2023, p. 106-139, 2023.

HAIG, B. D. Big data science: A Philosophy of Science Perspective. In: WOO, S. E.; TAY, L.; PROCTOR, R. W. (ed.). Big Data in Psychological Research. Washington: American Psychological Association, p. 15-33, 2020.

HALLIDAY, M. A. K. Language as Social Semiotic. London: Edward Arnold, 1978.

HATIM, B.; MASON, I. Discourse and the Translator. New York: Routledge, 2014.

HOUSE, J. Translation Quality Assessment: A Model Revisited. Tübingen: Gunter Narr, 1997.

JAKOBSON, R. The Metaphoric and Metonymic Poles. In: JAKOBSON, R.; HALLE, M. (ed.). Fundamentals of Language. The Hague/Paris: Mouton, p. 90-96, 1956.

JIANG, Y.; BAI, T. Studies in Analytic Philosophy in China. Synthese, v. 175, n. 1, p. 3-12, 2010.

JI, C.; LI, Y.; QIU, W. et al. Big Data Processing: Big Challenges and Opportunities. Journal of Interconnection Networks, v. 13, n. 3, p. 1-19, 2012.

JIN, X.; WAH, B. W.; CHENG, X. et al. Significance and Challenges of Big Data Research. Big Data Research, v. 2, n. 2, p. 59-64, 2015.

KAPCHAN, D. A. Performance + In folklore. Journal of American Folklore, v. 108, n. 430, p. 479-508, 1995.

KING, G.; KEOHANE, R. O.; VERBA, S. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton: Princeton University Press, 2021.

KITCHIN, R. Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society, v. 1, n. 1, p. 1-12, 2014.

LAKOFF, G.; JOHNSON, M. The Metaphorical Logic of Rape. Metaphor and Symbol, v. 2, n. 1, p. 73-79, 1987.

LAU, J. Y. F.; CHAN, J. K. L. A Brief History of Analytic Philosophy in Hong Kong. Asian Journal of Philosophy, v. 1, n. 1, p. 1-20, 2022.

LEONELLI, S. Data-Centric Biology: A Philosophical Study. Chicago: University of Chicago Press, 2016.

LOSONSKY, M. Linguistic Turns in Modern Philosophy. Cambridge: Cambridge University Press, 2006.

LUTZ, S. Artificial Language Philosophy of Science. European Journal for Philosophy of Science, v. 2, n. 2, p. 181-203, 2012.

MACHERY, E. Thought Experiments and Philosophical Knowledge. Metaphilosophy, v. 42, n. 3, p. 191-214, 2011.

MITTELSTADT, B. The Ethics of Biomedical ‘Big Data’ Analytics. Philosophy & Technology, v. 32, p. 17-21, 2019.

O’LEARY, D. E. Artificial Intelligence and Big Data. IEEE Intelligent Systems, v. 28, n. 2, p. 96-99, 2013.

OLSHER, D. Semantically-based Priors and Nuanced Knowledge Core for Big Data, Social AI, and Language Understanding. Neural Networks, v. 58, n. 10, p. 131-147, 2014.

ONG, Y. P. The Language of Advertising and the Novel: Naipaul's A House for Mr. Biswas, Twentieth Century Literature v. 56, n. 4, p. 462-492, 2010.

PREUS, A.; ANTON, J. P. Essays in Ancient Greek Philosophy V: Aristotle’s Ontology. New York: State University of New York Press, 1992.

RIZK, A.; ELRAGAL, A. Data Science: Developing Theoretical Contributions in Information Systems Via Text Analytics. Journal of Big Data, v. 7, p. 1-26, 2020.

RORTY, R. M. The Linguistic Turn: Recent Essays in Philosophical Method. Chicago: University of Chicago Press, 1967.

RUSSELL, B. On denoting. Mind, v. 14, n. 4, p. 479-493, 1905.

SÆTRA, H. S. Science as a Vocation in the Era of Big Data: The Philosophy of Science behind Big Data and Humanity’s Continued Part in Science. Integrative Psychological and Behavioral Science, v. 52, n. 4, p. 508-522, 2018.

SCHÖNBERGER, V. M.; CUKIER, K. Big Data: A Revolution that will Transform How We Live, Work and Think. New York: Houghton Mifflin Harcourt, 2013.

SEARLE, J. Speech Acts. An Essay in the Philosophy of Language. Cambridge: Cambridge University Press, 1969.

SHARDLOW, M.; SELLAR, S.; ROUSELL, D. Collaborative Augmentation and Simplification of Text (CoAST): Pedagogical Applications of Natural Language Processing in Digital Learning Environments. Learning Environment Research, v. 25, n. 2, p. 399-421, 2022.

SLOTA, S. C.; HOFFMAN, A. S.; RIBES, D. et al. Prospecting (in) the Data Sciences. Big Data & Society, v. 7, n. 1, p. 1-12, 2020.

SPENCER, M. Pali Grammar: The Language of the Canonical Texts of Theravada Buddhism, vol 1 Buddhist Studies Review, v. 37, n. 1, p. 117-126, 2020.

SPERBER, D.; WILSON, D. Relevance: Communication and Cognition. Cambridge, MA: Harvard University Press, 1986.

SUN, G.; LI, F.; JIANG, W. Brief Talk about Big Data Graph Analysis and Visualization. Journal on Big Data, v. 1, n. 1, p. 25-26, 2019.

SYMONS, J.; ALVARADO, R. Can We Trust Big Data? Applying Philosophy of Science to Software. Big Data & Society, v. 3, n. 2, p. 1-17, 2016.

TÖRNBERG, P.; TÖRNBERG, A. The Limits of Computation: A Philosophical Critique of Contemporary Big Data Research. Big Data & Society, v. 5, n. 2, p. 1-12, 2018.

WESTHAVER, G. Continuity and Development: Looking for Typological Treasure with William Jones of Nayland and E. B. Pusey. Bulletin of the John Rylands Library, v. 97, n 1, p. 161-177, 2021.

WIENER, N. Cybernetics, or Communication and Control in the Animal and the Machine. Cambridge, MA: MIT Press, 1948.

WITTGENSTEIN, L. Philosophical Investigations. G.E.M. Anscombe and R. Rhees (ed.), G.E.M. Anscombe (trans.). Oxford: Blackwell, 1953.

Received: 28/04/2023 - Approved: 01/07/2023 - Published: 10/01/2024

Pubblicato

2024-01-05

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Analisi del data turn nella filosofia del linguaggio nell’era dei big data. (2024). TRANS/FORM/AÇÃO:/Revista/De/Filosofia, 47(4), e0240050. https://revistas.marilia.unesp.br/index.php/transformacao/article/view/14517