CONDICIONES EPISTÉMICAS PARA LA CREACIÓN DE CONOCIMIENTO EN CIENCIAS HUMANAS
Palabras clave:
Conocimiento científico, Cognición científica, Psicología de la ciencia, EpistémicaResumen
Ciertos procesos ocurren en el aparato cognitivo del investigador cuando se ejecuta la investigación científica, los cuales afectan el acopio de los datos, su tratamiento y su interpretación. Tales procesos pueden ser analizados no sólo como meros hechos cognitivos, sino teniendo en cuenta su rol en la génesis, descubrimiento, manejo y transformación del conocimiento, esto es, desde el punto de vista epistémico. Las condiciones psicológicas de confiabilidad de tal conocimiento no son bien conocidas en la Psicología de la ciencia, y por ello aquí se presenta un nuevo modelo sobre los procesos epistémicos involucrados en el proceso referido, las condiciones epistémicas de posibilidad para la producción de conocimiento científico acerca de los seres humanos, y sus consecuencias para la empresa científica en las ciencias sociales y humanas.
Citas
Arecchi, F. (1999). Complexity versus Complex Systems: A New Approach to Scientific Discovery. En L. Magnani, N. Nersessian & P. Thagard (Eds.), Model-Based Reasoning in Scientific Discovery. Proceedings of the International Conference on Model-Based Reasoning in Scientific Discovery
(December 17-19, 1998, Pavia, ltaly). New York: Springer Science+Business Media.
Benítez, I. (2005). Técnicas de Agrupamiento para el Análisis de Datos Cuantitativos y Cualitativos. España: Universidad Politécnica de Valencia, Departamento de Ingeniería de Sistemas y Automática.
Brent, E., Slusarz, P. & Thompson, A. (2002). Qualrus™. The intelligent qualitative analysis program. Columbia, MS: Idea Works, Inc.
Brigandt, I. (2012). The Dynamics of Scientific Concepts: The Relevance of Epistemic Aims and Values. En U. Feest & F. Steinle (Eds.), Scientific Concepts and Investigative Practice (pp. 75–103). Berlin: de Gruyter. Recuperado de https://sites.ualberta.ca/~brigandt/The_dynamics_of_scientific_concepts.pdf
Brown, M. (2008) Science as socially distributed cognition: Bridging philosophy and sociology of science. En K. Francois, B. Löwe, T. Müller & B. Van Kerkhove (Eds.), Foundations of the Formal Sciences VII. Recuperado de: http://www.math.unihamburg.de/home/loewe/FotFS/VII/Book/FotFS_VII_Brown.pdf
Carifio, J. & Perla, R. (2013). Towards a Psychology of Science, Scientific Knowledge, and Scientific Change, Mark I. Journal of Science and Technology, 3(1), 53-60.
Carrier, M. (2010). Scientific Knowledge and Scientific Expertise: Epistemic and Social Conditions of Their Trustworthiness. Analyse & Kritik, 02(S), 195-212.
Carruthers, P., Stich, S. & Siegal, M. (Eds.). (2002). The cognitive basis of science. Cambridge: Cambridge University. doi: https://doi.org/10.1017/CBO9780511613517
Ceusters, W. & Smith, B. (2006). Referent Tracking for Treatment Optimisation in Schizophrenic Patients. Journal of Web Semantics, 4(3), 229–236. Recuperado de: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3583560/
Crane, T. & French, C. (2015). The Problem of Perception. En Zalta, E. (Ed.), The Stanford Encyclopedia of Philosophy. Recuperado de: http://plato.stanford.edu/archives/win2015/entries/perception-problem/
De Waard, A. & Pander, H. (2012). Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Korea, July 12, 47–55.
Dunbar, K. & Blanchette, I. (s/f). The InVivo/InVitro Approach to Cognition: The Case of Analogy. Recuperado de: http://www.cogsci.ucsd.edu/~coulson/203/dunbarTICS.pdf
Dunbar, K. (1999). How Scientists Build Models. InVivo Science as a Window on the Scientific Mind. En L. Magnani, L., N. Nersessian & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 89-98). New York: Plenum Press. doi: https://doi.org/10.1007/978-1-4615-4813-3_6
Dunbar, K. (1997). How scientists think: On-line creativity and conceptual change in science. En T. Ward, S. Smith & J. Vaid (Eds.), Conceptual structures and processes (pp. 461-493). Washington: American Psychological Association. doi: https://doi.org/10.1037/10227-017
Eraña, A. & Martínez, S. (2004). The Heuristic Structure of Scientific Knowledge. Journal of Cognition and Culture, 4(3), 701-729. doi: https://doi.org/10.1163/1568537042484878
Estany, A. (2001). Ventajas epistémicas de la cognición socialmente distribuida. Contrastes. Revista Internacional de Filosofía, 0. doi: http://dx.doi.org/10.24310/Contrastescontrastes.v0i0.1472
Feist, G. (2013). The psychology of scientific thought and behaviour. The Psychologist, 26(12), 864-867. Recuperado de: https://thepsychologist.bps.org.uk/volume-26/edition-12/psychology-scientific-thought-and-behaviour
Feist, G. (2006). The psychology of science and the origins of the scientific mind. New Haven and London: Yale University.
Fernando, I. & Henskens, F. (2013a). ST Algorithm for Medical Diagnostic Reasoning. Polibits, 48, 23-29. Recuperado de: http://polibits.gelbukh.com/2013_48/ST%20Algorithm%20for%20Medical%20Diagnostic%20Reasoning.pdf
Fernando, I. & Henskens, F. (2013b) Drill-Locate-Drill Algorithm for Diagnostic Reasoning in Psychiatry. International Journal of Machine Learning and Computing, 3(5), 449-452. https://doi.org/10.7763/IJMLC.2013.V3.358
Floridi, L. (2015). Semantic Conceptions of Information. En E. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Recuperado de: http://plato.stanford.edu/archives/spr2015/entries/information-semantic/
García, J. (2000). Informar y narrar: el análisis de los discursos en las investigaciones de campo. Revista de Antropología social, 9, 75-104.
Golafshani, N. (2003). Understanding Reliability and Validity in Qualitative Research. The Qualitative Report, 8(4), 597-607. Recuperado de: http://www.nova.edu/ssss/QR/QR8-4/golafshani.pdf
Gran, T. (2015). The Process of Speech-acting Specifies Methods for Grasping Meaning. Ten Operations. Journal of Applied Hermeneutics, May, 1-17. Recuperado de: http://jah.journalhosting.ucalgary.ca/jah/index.php/jah/article/download/91/pdf
Heintz, C. (2007). Scientific cognition and cultural evolution: theoretical tools for integrating cognitive and social studies of science (Tesis doctoral). Ecole des Hautes Etudes en Sciences Sociales, Paris.
Hofer, B. (2016). Epistemic Cognition as a Psychological Construct: Advancements and Challenges. En K. Iordanou, P. Kendeou, & K. Beker (Eds.), Handbook of Epistemic Cognition (Capítulo 2). London: Routledge.
Huber, G. & Gürtler, L. (2013). AQUAD 7: Manual del programa para analizar datos cualitativos. Tübingen: Softwarevertrieb Günter Huber. Recuperado de: http://www.aquad.de/materials/manual_aquad7/manual-c.pdf
Ibarra, A. (2012). Epistemic networks. New subjects for new forms of (scientific) knowledge production. Science, Technology & Innovation Studies, 8(1), 61-74. Recuperado de: http://www.sti-studies.de/ojs/index.php/sti/article/view/121/93
Liu, Z., Nersessian, N. & Stasko, J. (2008). Distributed Cognition as a Theoretical Framework for Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1173-1180. doi: https://doi.org/10.1109/TVCG.2008.121
Magnani, L. (2008). Discovering and Communicating through Multimodal Abduction, the Role of External Semiotic Anchors and Hybrid Representations. Studies in Computational Intelligence, 123, 41–62.
Magnus, P. (2007). Distributed Cognition and the Task of Science. Social Studies of Science, 37(2), 297–310. doi: https://doi.org/10.1177/0306312706072177
Maton, K. (2003). Pierre Bourdieu and the Epistemic Conditions of Social Scientific Knowledge. Space & Culture, 6(1), 52-65. doi: https://doi.org/10.1177/1206331202238962
Mitroff, I. & Kilmann, R. (1977). Systemic Knowledge: Toward an Integrated Theory of Science. Theory and Society, 4(1), 103-129. doi: https://doi.org/10.1007/BF00209746
Mulligan, K. & Correia, F. (2013). Facts. En E. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Recuperado de: http://plato.stanford.edu/archives/spr2013/entries/facts/
Nersessian, N. (2009). How Do Engineering Scientists Think? Model-Based Simulation in Biomedical Engineering Research Laboratories. Topics in Cognitive Science, 1, 730–757.
Nersessian, N. (2002a). The cognitive basis of model-based reasoning in science. En P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (Chapter 7). Cambridge: Cambridge University. Recuperado de: https://mechanism.ucsd.edu/teaching/F12/cs200/Readings/Nersessian.cognitivebasisofmodel-basedreasoninginscience.2002.pdf
Nersessian, N. (2002b). Model-based reasoning in conceptual change. Recuperado de: http://www.cc.gatech.edu/aimosaic/faculty/nersessian/papers/model-based-reasoning-in-conceptualchange.pdf
Pepe, A. (2008). Socio-epistemic analysis of scientific knowledge production in little science research. Triple C, 6(2), 134-145. Recuperado de: http://www.triple-c.at/index.php/tripleC/article/view/84
Reed, I. (2011). Interpretation and social knowledge. On the Use of Theory in the Human Sciences. Chicago & London: University of Chicago. doi: https://doi.org/10.7208/chicago/9780226706726.001.0001
Salgado, A. (2007). Investigación cualitativa: diseños, evaluación del rigor metodológico y retos. Liberábit, 13, 71-78.
Shalizi, C. (2006). Methods and techniques of complex systems science: an overview. En T. Deisboeck & J. Kresh (Eds.), Complex Systems Science in Biomedicine (Capítulo 1). New York: Springer. doi: https://doi.org/10.1007/978-0-387-33532-2_2
Thagard, P. (2012). The Cognitive Science of Science. Explanation, Discovery, and Conceptual Change. Cambridge: MIT Press.
Vasilachis, I. (2009). Los fundamentos ontológicos y epistemológicos de la investigación cualitativa. Forum: Qualitative Social Research, 10(2), Art. 30. Recuperado de: http://nbn-resolving.de/urn:nbn:de:0114-fqs0902307
Zytkow, J. (1999). Scientific Modeling: A Multilevel Feedback Process. En L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-Based Reasoning in Scientific Discovery (Proceedings of the International Conference on Model-Based Reasoning in Scientific Discovery, December 17-19, 1998, Pavia, ltaly). New York: Springer Science+Business Media.
(December 17-19, 1998, Pavia, ltaly). New York: Springer Science+Business Media.
Benítez, I. (2005). Técnicas de Agrupamiento para el Análisis de Datos Cuantitativos y Cualitativos. España: Universidad Politécnica de Valencia, Departamento de Ingeniería de Sistemas y Automática.
Brent, E., Slusarz, P. & Thompson, A. (2002). Qualrus™. The intelligent qualitative analysis program. Columbia, MS: Idea Works, Inc.
Brigandt, I. (2012). The Dynamics of Scientific Concepts: The Relevance of Epistemic Aims and Values. En U. Feest & F. Steinle (Eds.), Scientific Concepts and Investigative Practice (pp. 75–103). Berlin: de Gruyter. Recuperado de https://sites.ualberta.ca/~brigandt/The_dynamics_of_scientific_concepts.pdf
Brown, M. (2008) Science as socially distributed cognition: Bridging philosophy and sociology of science. En K. Francois, B. Löwe, T. Müller & B. Van Kerkhove (Eds.), Foundations of the Formal Sciences VII. Recuperado de: http://www.math.unihamburg.de/home/loewe/FotFS/VII/Book/FotFS_VII_Brown.pdf
Carifio, J. & Perla, R. (2013). Towards a Psychology of Science, Scientific Knowledge, and Scientific Change, Mark I. Journal of Science and Technology, 3(1), 53-60.
Carrier, M. (2010). Scientific Knowledge and Scientific Expertise: Epistemic and Social Conditions of Their Trustworthiness. Analyse & Kritik, 02(S), 195-212.
Carruthers, P., Stich, S. & Siegal, M. (Eds.). (2002). The cognitive basis of science. Cambridge: Cambridge University. doi: https://doi.org/10.1017/CBO9780511613517
Ceusters, W. & Smith, B. (2006). Referent Tracking for Treatment Optimisation in Schizophrenic Patients. Journal of Web Semantics, 4(3), 229–236. Recuperado de: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3583560/
Crane, T. & French, C. (2015). The Problem of Perception. En Zalta, E. (Ed.), The Stanford Encyclopedia of Philosophy. Recuperado de: http://plato.stanford.edu/archives/win2015/entries/perception-problem/
De Waard, A. & Pander, H. (2012). Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Korea, July 12, 47–55.
Dunbar, K. & Blanchette, I. (s/f). The InVivo/InVitro Approach to Cognition: The Case of Analogy. Recuperado de: http://www.cogsci.ucsd.edu/~coulson/203/dunbarTICS.pdf
Dunbar, K. (1999). How Scientists Build Models. InVivo Science as a Window on the Scientific Mind. En L. Magnani, L., N. Nersessian & P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp. 89-98). New York: Plenum Press. doi: https://doi.org/10.1007/978-1-4615-4813-3_6
Dunbar, K. (1997). How scientists think: On-line creativity and conceptual change in science. En T. Ward, S. Smith & J. Vaid (Eds.), Conceptual structures and processes (pp. 461-493). Washington: American Psychological Association. doi: https://doi.org/10.1037/10227-017
Eraña, A. & Martínez, S. (2004). The Heuristic Structure of Scientific Knowledge. Journal of Cognition and Culture, 4(3), 701-729. doi: https://doi.org/10.1163/1568537042484878
Estany, A. (2001). Ventajas epistémicas de la cognición socialmente distribuida. Contrastes. Revista Internacional de Filosofía, 0. doi: http://dx.doi.org/10.24310/Contrastescontrastes.v0i0.1472
Feist, G. (2013). The psychology of scientific thought and behaviour. The Psychologist, 26(12), 864-867. Recuperado de: https://thepsychologist.bps.org.uk/volume-26/edition-12/psychology-scientific-thought-and-behaviour
Feist, G. (2006). The psychology of science and the origins of the scientific mind. New Haven and London: Yale University.
Fernando, I. & Henskens, F. (2013a). ST Algorithm for Medical Diagnostic Reasoning. Polibits, 48, 23-29. Recuperado de: http://polibits.gelbukh.com/2013_48/ST%20Algorithm%20for%20Medical%20Diagnostic%20Reasoning.pdf
Fernando, I. & Henskens, F. (2013b) Drill-Locate-Drill Algorithm for Diagnostic Reasoning in Psychiatry. International Journal of Machine Learning and Computing, 3(5), 449-452. https://doi.org/10.7763/IJMLC.2013.V3.358
Floridi, L. (2015). Semantic Conceptions of Information. En E. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Recuperado de: http://plato.stanford.edu/archives/spr2015/entries/information-semantic/
García, J. (2000). Informar y narrar: el análisis de los discursos en las investigaciones de campo. Revista de Antropología social, 9, 75-104.
Golafshani, N. (2003). Understanding Reliability and Validity in Qualitative Research. The Qualitative Report, 8(4), 597-607. Recuperado de: http://www.nova.edu/ssss/QR/QR8-4/golafshani.pdf
Gran, T. (2015). The Process of Speech-acting Specifies Methods for Grasping Meaning. Ten Operations. Journal of Applied Hermeneutics, May, 1-17. Recuperado de: http://jah.journalhosting.ucalgary.ca/jah/index.php/jah/article/download/91/pdf
Heintz, C. (2007). Scientific cognition and cultural evolution: theoretical tools for integrating cognitive and social studies of science (Tesis doctoral). Ecole des Hautes Etudes en Sciences Sociales, Paris.
Hofer, B. (2016). Epistemic Cognition as a Psychological Construct: Advancements and Challenges. En K. Iordanou, P. Kendeou, & K. Beker (Eds.), Handbook of Epistemic Cognition (Capítulo 2). London: Routledge.
Huber, G. & Gürtler, L. (2013). AQUAD 7: Manual del programa para analizar datos cualitativos. Tübingen: Softwarevertrieb Günter Huber. Recuperado de: http://www.aquad.de/materials/manual_aquad7/manual-c.pdf
Ibarra, A. (2012). Epistemic networks. New subjects for new forms of (scientific) knowledge production. Science, Technology & Innovation Studies, 8(1), 61-74. Recuperado de: http://www.sti-studies.de/ojs/index.php/sti/article/view/121/93
Liu, Z., Nersessian, N. & Stasko, J. (2008). Distributed Cognition as a Theoretical Framework for Information Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1173-1180. doi: https://doi.org/10.1109/TVCG.2008.121
Magnani, L. (2008). Discovering and Communicating through Multimodal Abduction, the Role of External Semiotic Anchors and Hybrid Representations. Studies in Computational Intelligence, 123, 41–62.
Magnus, P. (2007). Distributed Cognition and the Task of Science. Social Studies of Science, 37(2), 297–310. doi: https://doi.org/10.1177/0306312706072177
Maton, K. (2003). Pierre Bourdieu and the Epistemic Conditions of Social Scientific Knowledge. Space & Culture, 6(1), 52-65. doi: https://doi.org/10.1177/1206331202238962
Mitroff, I. & Kilmann, R. (1977). Systemic Knowledge: Toward an Integrated Theory of Science. Theory and Society, 4(1), 103-129. doi: https://doi.org/10.1007/BF00209746
Mulligan, K. & Correia, F. (2013). Facts. En E. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Recuperado de: http://plato.stanford.edu/archives/spr2013/entries/facts/
Nersessian, N. (2009). How Do Engineering Scientists Think? Model-Based Simulation in Biomedical Engineering Research Laboratories. Topics in Cognitive Science, 1, 730–757.
Nersessian, N. (2002a). The cognitive basis of model-based reasoning in science. En P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (Chapter 7). Cambridge: Cambridge University. Recuperado de: https://mechanism.ucsd.edu/teaching/F12/cs200/Readings/Nersessian.cognitivebasisofmodel-basedreasoninginscience.2002.pdf
Nersessian, N. (2002b). Model-based reasoning in conceptual change. Recuperado de: http://www.cc.gatech.edu/aimosaic/faculty/nersessian/papers/model-based-reasoning-in-conceptualchange.pdf
Pepe, A. (2008). Socio-epistemic analysis of scientific knowledge production in little science research. Triple C, 6(2), 134-145. Recuperado de: http://www.triple-c.at/index.php/tripleC/article/view/84
Reed, I. (2011). Interpretation and social knowledge. On the Use of Theory in the Human Sciences. Chicago & London: University of Chicago. doi: https://doi.org/10.7208/chicago/9780226706726.001.0001
Salgado, A. (2007). Investigación cualitativa: diseños, evaluación del rigor metodológico y retos. Liberábit, 13, 71-78.
Shalizi, C. (2006). Methods and techniques of complex systems science: an overview. En T. Deisboeck & J. Kresh (Eds.), Complex Systems Science in Biomedicine (Capítulo 1). New York: Springer. doi: https://doi.org/10.1007/978-0-387-33532-2_2
Thagard, P. (2012). The Cognitive Science of Science. Explanation, Discovery, and Conceptual Change. Cambridge: MIT Press.
Vasilachis, I. (2009). Los fundamentos ontológicos y epistemológicos de la investigación cualitativa. Forum: Qualitative Social Research, 10(2), Art. 30. Recuperado de: http://nbn-resolving.de/urn:nbn:de:0114-fqs0902307
Zytkow, J. (1999). Scientific Modeling: A Multilevel Feedback Process. En L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-Based Reasoning in Scientific Discovery (Proceedings of the International Conference on Model-Based Reasoning in Scientific Discovery, December 17-19, 1998, Pavia, ltaly). New York: Springer Science+Business Media.
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24-03-2018
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Velasco Castro, A. (2018). CONDICIONES EPISTÉMICAS PARA LA CREACIÓN DE CONOCIMIENTO EN CIENCIAS HUMANAS. LÍMITE Revista Interdisciplinaria De Filosofía Y Psicología, 13(41). Recuperado a partir de https://revistalimite.uta.cl/index.php/limite/article/view/91
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Artículos de Investigación
