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Special Social Considerations for Science

Special Social Considerations for Science. November 17,2004 Howard Burrows Autonomous Undersea Systems Institute Lee, NH. Howard Burrows. UVM – Physiology & Biophysics (nerve studies PhD) NIH – Brain scans and synapse formation NASA – Earth science and digital libraries

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Special Social Considerations for Science

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  1. SpecialSocial Considerations for Science November 17,2004 Howard BurrowsAutonomous Undersea Systems InstituteLee, NH

  2. Howard Burrows UVM – Physiology & Biophysics (nerve studies PhD) NIH – Brain scans and synapse formation NASA – Earth science and digital libraries NSF – National Science Digital Library (NSDL)

  3. Outline Myths of science Science as a process Social issues in science Science and social computing

  4. Myths of Science • It is not (entirely) true that science: • Discovers facts • Builds knowledge • Answers questions • Depends on data and observation

  5. A common myth • The business of science is to add value to: • Raw sensory input • Discover implications as new concepts • Translate into words and propositions • Reach conclusions through reasoning

  6. Data, Community, Public Use Backbone data centers Science data provider Application solution provider Users NCDC Johns Hopkins USDA Farmers GHRC USGS Technology provider IBM Courtesy Yuan-Chi Chang, IBM

  7. Science Economy (fireball)

  8. Outline Myths of science Science as a process Social issues in science Science and social computing

  9. What is science related to? • Science is related to: • Technology • Engineering • Medicine • But more closely to: • Philosophy • Language • Mathematics

  10. Knowledge/Science Compared • Knowledge • Justified • True • Belief • A thing • Science • Hypothesize • Observe • Evaluate • A process

  11. Changing Views of Science 1930 – Sense and Reason (logical positivism) 1960 – Beyond Reason (linguistic turn) 1990 – Social Construction (peer review) 2020 – Back to Reason and Sense

  12. Linguistic turn Word meanings are unstable: e.g. “Mother!” All words are like pronouns See that guy. Call him “Ralph” See those measurements. Call them “Global warming” Words are not “defined” They are gestures at an unspecified topic.

  13. Words as “Radial Concepts” The abstract category “crow” is generated from a single example of a crow. “That crow” is elaborated to the category “crow” by relaxing the designation to include another crow. The schema for elaboration is never fully specified. “Crow” has a plurality of meaning by ephemeral shifts in both the example and the schema.

  14. Vision Concept-based science communication Personalized Learning Environment Economic Environment supports learning

  15. Two models for Science • Science I • Data • Logic • Knowledge • A production chain • Science II • Hypothesize • Observe • Evaluate • An iterative loop

  16. Ontology, Knowledge, Value Ontology – what is taken to exist (what needs a name) Knowledge – what do you know about it Spin (value) – thesis, antithesis, synthesis

  17. Discourse concerningOntology and Knowledge What counts as data when you form your beliefs The relation between evidence and judgement Are your beliefs “sensible” and “reasonable” The relation between metrics and evaluation

  18. Outline Myths of science Science as a process Social issues in science Science and social computing

  19. NASA Dynamic Archives Courtesy Don Collins of the DAAC Alliance

  20. The Age of Unreason “Changes are not what they used to be.” from book “The Age of Unreason” Charles Handy, 1989 • Change by discontinuous leaps • Learning from the past dangerous • Evolution yes, but allow for revolution

  21. Social uses of Science Improving health Informing policies Solving crimes

  22. Science Economy (fireball)

  23. Information Partners Federation Science Oceanography (2) Terrestrial Studies (4) Climate (3) Technology (3) Public Use • Education (3) • Regional Policy (4) • Public Health • Media • Legal

  24. Community I • “A united system of knowledge is the surest means of identifying the still unexplored domains of reality. It provides a clear map of what is known, and it frames the most productive questions for future inquiry.” E. O. Wilson, 1998 Consilience: The Unity of Knowledge

  25. Community II • “It is the disorder of the scientific community—the laminated, finite, partially independent strata supporting one another; it is the disunification of science—the intercalation of different patterns of argument—that is responsible for its strength and coherence..” Peter Galison , 1997 Image and Logic

  26. Federalism Central coordination, local autonomy Tiered governance (US Federal vs States) Yield power to center (only reluctantly) Heterogeneous, diverse communities Data centers, academics, government, and industry Interdependence & minority interests Match and balance different values Take into account intensity of interest The whole is greater than the parts.

  27. Changing Views of Science 1930 – Sense and Reason (logical positivism) 1960 – Beyond Reason (linguistic turn) 1990 – Social Construction (peer review) 2020 – Back to Reason and Sense

  28. US National Science FoundationReview Criteria Intellectual merit creative and original concepts Broader impacts benefits to society

  29. Evaluating Peer Review Selecting reviewers; providing incentives Informed objectivity; conflict of interest Review criteria; comparing across panels Ronald N. KostoffUS Office of Naval Research

  30. Selecting Reviewers: Peer vs Non-Peer Balancing interests: Economic- government, academia, industry, society Intellectual- engineers, scientists, policy agencies Practical- curiosity, problem based, market driven

  31. Informed objectivity “Good old boy” consensus Natural bias often subtle, needs diversity With paradigm shifts, experience becomes a liability

  32. Changing Views of Science 1930 – Sense and Reason (logical positivism) 1960 – Beyond Reason (linguistic turn) 1990 – Social Construction (peer review) 2020 – Back to Reason and Sense

  33. Fact-value continuum? Can we distinguish fact claims from value judgments? Are there really objective as opposed to subjective distinctions? Amartya Sen introduces value judgments in economics

  34. Fate of knowledge Knowledge is social Cognitive processes are social (reasonable) Actions based on knowledge are justified through social processes.

  35. Back to the Vision Concept-based science communication Personalized Learning Environment Economic Environment supports learning

  36. Science in solitude • Is it possible? • What about common vocabulary? • Is a concept possible? • Is scientific justification possible? • What about peer review?

  37. Outline Myths of science Science as a process Social issues in science Science and social computing

  38. Concept spaces;concept mapping

  39. Registries and Markup Languages Service Oriented Architectures Web services and “deep” web “Swoogle” and registered namespaces and ontologies Specialized markup languages (MathML, ChemML)

  40. structure Data Structure Features Models Semantic Images Video Audio Multimedia Formats Layout Regions Segments Mosaics Relationship(Spatio-temporal) Color Texture Shape Motion Camera motion Clusters Classes Collections Probabilities Confidences Objects Events Actions People Labels Relationship MPEG-7: Metadata for Content Description Signal Data Features Model Semantics Courtesy John Smith, IBM

  41. Re-Use Data reuseability – “My noise is your data” Intellectual property and “Future of the Idea” Granularity and interfaces – nonlinear presentations Persistent archives and technical issues

  42. Promising developments Semantic web not words; rather “meaningful” data, concepts, and ideas. Science draws meaning from data; and has changed the way it justifies this. The semantic web offers to improve or supplant “peer” review (and education). The semantic web provides a “marketplace” for learning.

  43. Back to the Vision Concept-based science communication Personalized Learning Environment Economic Environment supports learning

  44. Business Plan and “Marketplace” Sales agents – Know-bots negotiate deals Value chains – Multiple entities in assembly line Public choice – Market forces and “fair” voting rules Learning Economy – From food chain to ecosystem

  45. Problems in Public Funding National Archives - GPRA - Curriculum - 2% GNP - What data is valuable? Is science making progress? What is “educated”? How much to spend?

  46. Trading Zones Economic- government, academia, industry Intellectual- engineers, scientists, policy agencies Practical- curiosity, problem based, market driven

  47. Summary Myths of science Science as a process Social issues in science Science and social computing

  48. Vision Concept-based science communication Personalized Learning Environment Economic Environment supports learning

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