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Let's talk about Philosophy of Science

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Philosophy of Science — Key Points

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1. Central questions - What distinguishes science from non-science? (Demarcation problem) - How do scientific theories explain and predict phenomena? - What is the nature of scientific reasoning (induction, deduction, abduction)? - How do observation and theory interact (theory-ladenness of observation)? - What is the status of scientific knowledge (realism vs. anti-realism)? 2. Major positions - Scientific Realism: Best scientific theories approximately truthfully describe unobservable entities (Putnam, Boyd). - Instrumentalism/Anti-Realism: Theories are tools for prediction, not necessarily true descriptions (van Fraassen). - Constructivism and Social Epistemology: Scientific knowledge is shaped by social processes, values, and institutions (Kuhn, Latour). - Structuralism and Model-Based Views: Science advances via models and structures rather than literal true statements (Suppe, Cartwright). 3. Methodology and reasoning - Induction: Generalizing from observations—problem of justifying induction (Hume). - Falsificationism: Popper’s idea that theories are scientific if falsifiable; emphasizes bold conjectures and refutations. - Bayesianism: Probabilistic updating of belief based on evidence. - Lakatos’ Research Programmes: Science progresses through competing research programmes with heuristics and protective belts. 4. Explanation and laws - Covering-law model: Explanations subsume phenomena under general laws (Hempel). - Causal/mechanistic accounts: Explanations cite causes or mechanisms producing phenomena (Salmon, Craver). - Pragmatic and pluralist views: Multiple types of explanation depending on context. 5. Values, objectivity, and ethics - Science aims for objectivity, but choices about methods, significance, and application involve epistemic and non-epistemic values. - Responsible research requires transparency, reproducibility, and ethical reflection. 6. Contemporary issues - Replication crisis and reliability of findings. - Role of models, simulations, and big data. - Science policy, public trust, and science communication. Further reading (concise) - Peter Godfrey-Smith, Theory and Reality (2003) - Karl Popper, The Logic of Scientific Discovery (1959) - Thomas Kuhn, The Structure of Scientific Revolutions (1962) - Bas van Fraassen, The Scientific Image (1980) If you want, I can explain any one of these points in more detail or compare specific positions (e.g., realism vs. van Fraassen).

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Responsible Research: Transparency, Reproducibility, and Ethical Reflection

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Responsible research rests on three interlocking commitments: - Transparency: Researchers should disclose methods, data, assumptions, and conflicts of interest so others can evaluate how results were produced. Transparency enables scrutiny, reduces biases, and builds public trust. (See: Stodden et al., "Toward Reproducible Computational Research," 2013.) - Reproducibility: Findings should be replicable by independent investigators using the same or similar methods and data. Reproducibility tests the reliability of results and helps distinguish robust knowledge from artefacts of chance, error, or selective reporting. (See: National Academies of Sciences, "Reproducibility and Replicability in Science," 2019.) - Ethical reflection: Researchers must consider broader moral implications—risks to participants, societal impacts, dual-use concerns, and justice in who benefits or is harmed. Ethical reflection guides choices about study design, dissemination, and application, preventing harm and aligning research with social values. (See: Resnik, "The Ethics of Science," 1998; UNESCO Recommendation on Science and Scientific Researchers, 2017.) Together, these elements ensure scientific practices produce reliable knowledge while respecting persons and society. Lack of any one undermines credibility: opacity hides errors or bias, irreproducible results waste resources and mislead, and absent ethical reflection can produce harmful or unjust applications.

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