Among the many factors offered to explain the initiation of science projects curios-ity looms large. Blackwell ( 1969 :130) put this view succinctly, “an aroused curiosity is the driving force behind inquiry” (see also Gruender 1971 ). More recently, Hoffmann ( 2011 ) insisted that projects arise when an investigator fi nds something “interesting.” What makes something interesting? He mentions an excit-ing possibility, an unexplained phenomenon, an anomaly, a puzzle. These are all plausible paths to science projects, and examples of them abound, especially in investigator- originated projects in universities. However, that an investigator fastens on something interesting is neither a necessary nor a suffi cient condition for starting a project. Even so, I do not go so far as Smith’s ( 1971 :147–148) claim that
“discovery of new effects inspired only by curiosity is by its very nature rare.”
In modern times, especially, many science projects are sponsor-originated : a government agency, organization, or corporation specifi es a project it will fund
23
because the outcome is expected to enhance war-making capabilities, public health, product development, and so forth. The outside investigator who signs on for the project has a fi nancial stake in doing the research but may have little intellectual interest. Likewise, employees of corporations and governmental agencies are often assigned projects in which, at least initially, their interest may be slight. Also, sovereigns and superrich people sponsor projects and hire people to carry them out.
In sponsor-originated science projects, investigators and sponsors may have differ-ent motivations, and intellectual curiosity may not be important for either group.
Beginning in early modern times, an investigator who solved a widely recog-nized problem, such as determining longitude at sea or making a long-lasting bat-tery, might receive a prize, government pension, prestigious position, or peer recognition. Reward-oriented projects are common today, and the range of rewards is large. Watson and Crick could have attempted to solve the structure of any num-ber of organic molecules, but they chose DNA, correctly anticipating that the solu-tion would garner for them a Nobel Prize ( Watson 1968 ). People tend to fl ock to a
“hot” area, such as high-temperature superconductors, carbon nanotubes, and quan-tum computing, because a major discovery may lead to prestige, grants, prizes, lucrative employment, perhaps even a fortune if the discovery—perhaps a recipe—
can be patented and sold or licensed. How else do we explain why many investiga-tors set aside an ongoing project in favor of one that is trendier? Intellectual interest of course may grow rapidly in reward-oriented projects owing to the intriguing problems they present.
On the basis of discussions earlier in this chapter, I suggest that the single great-est spur to science projects—in the past and present—is technology projects, which produce recipes and also may generate subsidiary science projects. Technology projects, whether in prehistory or the present, do not end with the development of the technology itself, for additional generalizations are needed to permit its compe-tent operation, maintenance, and perhaps reuse or discard. To this day, investigators at Los Alamos National Laboratory study the deterioration of America’s nuclear weapons, creating new science as needed; and the search for a permanent repository for nuclear waste has generated many projects in geology and materials science.
Regardless of the sponsor’s or investigator’s motivations, many a science project begins with a question or a problem. To wit, something is problematized with the expectation that a project may provide a solution. Problems and questions are equiv-alent ways of framing a project’s starting point, and both specify the scientifi c knowledge being sought. When a question or problem is implicit in a past project, we may model it. In the chapter “Discovery Processes: Trial Models,” I discuss other ways that projects begin.
I emphasize that new scientifi c knowledge may arise in any societal context when people develop a new technology. In the past, worshiping gods, crossing the seas, hunting large game, gathering root vegetables, and irrigating a fi eld all inspired new technologies whose subsidiary projects created new science. This perspective gives us a mandate to document or infer the science generated during any technol-ogy’s life history. Let us not forget that technological activities of every kind are knowledge-intensive (Layton 1974 ; Schiffer 1992 , 2011 ).
Why Do People Undertake Science Projects?
References
Barnes, Barry, David Bloor, and John Henry. 1996. Scientifi c knowledge: A sociological analysis . Chicago: University of Chicago Press.
Blackwell, Richard J. 1969. Discovery in the physical sciences . Notre Dame, IN: University of Notre Dame Press.
Galison, Peter. 2003. Einstein’s clocks, Poincaré’s maps: Empires of time . New York: W. W. Norton.
Garber, Elizabeth (ed.). 1990. Beyond history of science: Essays in honor of Robert E. Schofi eld . Bethlehem, PA: Lehigh University Press.
Gooding, David. 1990. Experiment and the making of meaning: Human agency in scientifi c obser-vation and experiment . Dordrecht: Kluwer.
Gruender, C. David. 1971. On distinguishing science and technology. Technology and Culture 12:
456–463.
Hoffmann, Roald. 2011. That’s interesting. American Scientist 99: 374–377.
Kuhn, Thomas. 1970. The structure of scientifi c revolutions , 2nd ed. Chicago: University of Chicago Press.
Layton, Edwin. 1971. Mirror-image twins: The communities of science and technology in 19th- century America. Technology and Culture 12: 562–580.
———. 1974. Technology as knowledge. Technology and Culture 15:31–41.
Molella, Arthur P., and Nathan Reingold. 1973. Theorists and ingenious mechanics: Joseph Henry defi nes science. Science Studies 3: 323–351.
Reid, J. Jefferson, Michael B. Schiffer, and William L. Rathje. 1975. Behavioral archaeology: Four strategies. American Anthropologist 77: 864–869.
Roller, Duane H.D. (ed.). 1971. Perspectives in the history of science and technology . Norman:
University of Oklahoma Press.
Rothbart, David. 2007. Philosophical instruments: Minds and tools at work . Urbana: University of Illinois Press.
Schiffer, Michael B. 1975. Behavioral chain analysis: Activities, organization, and the use of space.
In chapters in the prehistory of Eastern Arizona, IV. Fieldiana: Anthropology 65: 103–119.
———. 1992. Technological perspectives on behavioral change . Tucson: University of Arizona Press.
———. 2003. Properties, performance characteristics and behavioral theory in the study of tech-nology. Archaeometry 45:169–172.
———. 2010. Behavioral archaeology: Principles and practice . London: Equinox.
———. 2011. Studying technological change: A behavioral approach . Salt Lake City: University of Utah Press.
Schiffer, Michael B., and Andrea R. Miller. 1999. The material life of human beings: Artifacts, behavior, and communication . London: Routledge.
Schiffer, Michael B., and James M. Skibo. 1987. Theory and experiment in the study of techno-logical change. Current Anthropology 28: 595–622.
———. 1997. The explanation of artifact variability. American Antiquity 62: 27–50.
Shapin, Steven. 1996. The scientifi c revolution . Chicago: University of Chicago Press.
Shapin, Steven, and Simon Schaffer. 1985. Leviathan and the air-pump: Hobbes, Boyle, and the experimental life . Princeton, NJ: Princeton University Press.
Skibo, James M., and Michael B. Schiffer. 2008. People and things: A behavioral approach to material culture . New York: Springer.
Smith, Cyril S. 1971. Art, science, and technology: Notes on their historical interaction. In Perspectives in the history of science and technology , ed. Duane H.D. Roller, 129–165.
Norman: University of Oklahoma Press.
Walker, William H., James M. Skibo, and Axel E. Nielsen. 1995. Introduction: Expanding archae-ology. In Expanding archaeology , eds. James M. Skibo, William H. Walker, and Axel E.
Nielsen, 1–12. Salt Lake City: University of Utah Press.
Watson, James D. 1968. The double helix: A personal account of the discovery of the structure of DNA. New York: Atheneum.
25 M.B. Schiffer, The Archaeology of Science, Manuals in Archaeological Method,
Theory and Technique 9, DOI 10.1007/978-3-319-00077-0_3,
© Springer International Publishing Switzerland 2013
Many science scholars emphasize the kinds of knowledge that make possible explanation. However, I regard explanation as secondary to prediction because virtually every human activity in every society moves forward on the basis of the predictions that scientifi c knowledge enables (cf. Reichenbach 1966 ; Schiffer and Miller 1999 ). Accordingly, this chapter shows, through defi nitions, examples, and discussions, the predictive capabilities of the several major varieties of scientifi c knowledge— as defi ned behaviorally .
Scientifi c knowledge consists of two major domains: descriptions (observations, categories, and classifi cations) and generalizations (empirical generalizations, experimental laws, recipes, theories, and models). As cognitive structures, descrip-tions and generalizadescrip-tions have to be modeled by the archaeologist on the basis of behavioral and material evidence . The modeling process allows us to study the scientifi c knowledge of nonliterate societies as well as the implicit knowledge found even in modern science.
In the actual conduct of science, the varieties of knowledge are interdependent.
Thus, one can neither defi ne an observation without mentioning categories nor dis-cuss categories without referring to empirical generalizations. And all generaliza-tions necessarily incorporate categories and descripgeneraliza-tions.