Nicolaus Copernicus University in Toruń
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Abstract: 
For a variety of causes, scientific information is often inaccurate, poorly empirically supported, and not as relevant as it should be. And although there are good reasons for still aiming for accurate, empirically supported and relevant information in the sciences, the defective character of scientific data is not only ubiquitous, but inevitable.

However, while the presence of defective information in science tends to be naturally seen as part of the dynamics of scientific development, it is a fact that the larger the defectiveness of the information that scientists work with, the less justified they are in trusting such information.

This course aims at addressing the different ways in which defective data is used in the sciences as well as the epistemic benefits and dangers associated with such toleration.  

Three specific objectives of the course are: 

• To provide a systematic overview of the different views in which defective data is tolerated, explained and understood in the sciences.
• To explain under which circumstances defective data could be considered reliable in scientific contexts.
• To discuss some case studies (from different scientific disciplines) that illustrate the above.

In order to do so, the course addresses three main questions: what is to tolerate defective data? Can we understand defective data? When is defective data reliable in scientific contexts? 
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Sessions

Session 1:
What is to tolerate defective data?

1. Generalities of defective data
2.    Unreliability of defective data
3.   Toleration of defective data
4.      Case studies: Anomaly in the perihelion of Mercury // Stagflation

Readings (“*" obligatory)

•  Bueno, O. (2017): “Scientific pluralism, consistency preservation, and inconsistency toleration", HUMANA. MENTE Journal of Philosophical Studies, 10(32), 229–245.
• *Martinez-Ordaz, M. del R. (2020): “The ignorance behind inconsistency toleration" in S.I. Knowing the Unknown, Synthese.
  PREPRINT

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Sesión 2:
Presentación de argumentos en las ciencias
​a. Cómo argumentan los científicos  
b. Evaluación y Refutación de argumentos científicos.
c. El caso SARS-COV-2 y los argumentos sobre riesgo.

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Recursos digitales

SESIÓN 1
Material para consulta obligatoria antes de la SESIÓN 

​Aliseda-Llera, A.  (2006): Abductive Reasoning Logical Investigations into Discovery and Explanation (Cap. 3), Springer: 

aliseda_-_abductive_reasoning
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SESIÓN 2
MATERIAL PARA CONSULTA OBLIGATORIA ANTES DE LA SESIÓN ​

LOS VIDEOS DE ESTA SESIÓN ESTARÁN DISPONIBLES UNA SEMANA ANTES

tippett2009__
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Referencias

Referencias básicas:
29 octubre 2021

1. Atocha Aliseda-Llera (2006): Abductive Reasoning Logical Investigations into Discovery and Explanation (Cap. 3), Springer: 53-94.

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05 noviembre 2021

1. Christine Tippett (2009): " Argumentation: The Language of Science ", Journal of Elementary Science Education, Vol. 21, No. 1 (Winter, 2009): 17-25.​

Referencias Complementarias:

1. ​​S. Erduran  (2007): "Methodological Foundations in the Study of Argumentation in Science Classrooms". en: Erduran S., Jiménez-Aleixandre M.P. (eds) Argumentation in Science Education. Science & Technology Education Library, vol 35. Springer, Dordrecht​​.
2. Erduran, S., M.P. Jiménez-Aleixandre (eds.)(2007), Argumentation in Science Education Perspectives from Classroom-Based Research, Science & Technology Education Library book series (CTISE, volume 35).
3. Martínez Ordaz, M. del R. y G.Ramos García (2020): "La apuesta de Pascal aplicada al COVID-19: Reflexiones filosóficas en torno al riesgo", Aion.mx, (51).
4. Morado, R. (2003). Racionalidad y lógicas no deductivas. Iztapalapa: Revista de Ciencias Sociales y Humanidades, (54):131–144. 
5. Gilbert Harman (1984): “Logic and reasoning”, Synthese 60: 107–127.
6. Aliseda, A. (2004). Logics in scientific discovery. Foundations of Science, 9(3):339–363.
7. Psillos, S. (2000): “Abduction: Between conceptual richness and computational complexity” en  Abduction and induction: 59–74. 
   
8. de Ridder, J. (19 oct 2020):  "Online illusions of understanding", Open for Debate:. 
   
9. Christensen, D. (2007). Epistemology of Disagreement: The Good News. The Philosophical Review, 116(2), 187-217. 
10. Sullivan, E. (2017). Understanding: not know-how. Philosophical Studies, 175(1), 221–240.
11. Paté-Cornell,Elisabeth (1996): Uncertainties in risk analysis: Six levels of treatment, Reliability Engineering & System Safety​ Volume 54, Issues 2–3: 95-111.

SARS-COV-2

1. SARS-COV-2 (Wikipedia)
2. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020​.
3. Age, Sex, Existing Conditions of COVID-19 Cases and Deaths
4. The Covid-19 risks for different age groups, explained
5. 44 Of The 70 Austinites Who Flew To Mexico For Spring Break Have Now Tested Positive For COVID-19.​

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