Transcript English Men of Science

The Culture of Numbers:
Origins and Development
Of Statistics on Science,
Technology and Innovation
BENOÎT GODIN
Seminar on
Research and Higher Education Policy
In Europe and in Czech Republic
Prague
27 November 2008
Introduction
• Statistics on science as an “industry”
– Governments: manuals, surveys, rankings
– Academics
• economics and the productivity of research
• bibliometrics
• Huge impact on how we understand
(and support) science
Introduction (continued)
• Three phases
– Emergence, 1869-1920 (scientists)
– Institutionalization, 1920-1960 (governments)
– Internationalization, 1960 and after (UNESCO,
OECD, European Commission)
• Type of statistics depend on context
– Eugenics (statistics on men of science)
– Professionalization of science
– Management and policy (accounting)
Eugenics
• Improving the quality of the population
• Contribution of great men to civilization
– Measuring the number of scientists
• Reproduction (family, Nation), or
« productivity »: demography
• Distribution: geography
– Measuring the output
• Productivity
• Performance
Eugenics (continued)
• Francis Galton (1822-1911)
– Statistician: correlation, regression
– Hereditary Genius (1869)
• Men of science as exceptionnally productive of eminent sons
– English Men of Science (1874) and Noteworthy Families
(1906)
• Genealogical analysis of 300 men of science, plus survey
among 100 members of the Royal Society
• Men of science have less children than their parents had
(tendency to extinction, danger to the race)
– Debate with the Swiss biologist A. de Candolle on
measuring « productivity »
Professionalization
• Improving the social conditions of scientists
• James McKeen Cattell (1860-1944)
– Psychologist
– Editor of Science (1895-1944)
– Plea for the « scientific study of science »
(statistics)
– From nature (1903) to nurture (1906 and after)
Professionalization (continued)
– American Men of Science (1906)
• Tool for funding men of science
• 4 000 names (34 000 in 1944)
• Statistics
– Productivity (quantity)
» Universities, States, Nations
– Performance (quality)
» Stars (peers as opposed to elite Academies)
» Distribution according to exponential law
» Ranking of universities: gains and looses
Professionalization (continued)
• Psychologists
– Counting papers as an indicator of the
« scientificity », or advancement of the
discipline
– « Taking stock of progress » (S.W.
Fernberger, from 1932 to 1956)
– « Whether or not advance has been
satisfactory » (S.I. Franz, 1917): unequal
productivity among researchers; dilettantes
Accounting
• First institutional statistics emerged in the
United States, then Canada, then Great
Britain
– Persuading firms to invest in research (US NRC);
management of industrial laboratories (R.N.
Anthony);
– Planning government support to science (war;
policy)
• Controlling expenses (Bureau of Budget)
• Investments
Accounting (continued)
• Money spent on research
– GERD (Bernal, 1939; US Departments; OECD)
• The most cherished indicator
• A statistical construction
– GERD/GDP (Levi, 1869; Bernal, 1939)
• Policy decisions
• Allocation of resources to R&D: optimal level
• Equilibrium between priorities (basic versus applied
research): balance
• Efficiency: input/output
– First: productivity and the production function
– Then: innovation
Accounting (continued)
• Impact: What Is Science?
– Research (rather than knowledge)
• Factor of progress
• Methodology: easily measurable (money, personnel, time)
– R&D (mainly D)
• Volume of industrial research
• Methodology: boundaries
– Systematic (rather than ad hoc)
• From inventors to organizations (laboratory)
• Methodology: costs of survey; book-keeping practices
– Creativity (versus routine: RSA)
• USSR; UNESCO efforts (RSA essential to ST)
• Innovation as a large concept
Accounting (continued)
• Impact: Obsession for economics,
including evolutionists
– Statistics (and their manuals)
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Expenditures on research (1962)
Technological balance of payments (1990)
Patents (1994)
Marketed innovation (1992)
High technology products
Productivity (2001)
Accounting (continued)
– Conceptual frameworks
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(Linear model of) innovation
Competitiveness and globalization
Economic Growth and productivity
National System of Innovation
Knowledge-Based Economy
Information Society
Accounting (continued)
• How Does a Framework Work?
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Premise: STI is good for you and society
Something new is happening (CHANGE)
It is quite different from the past
Let’s call this change (NEW NAME)
It will bring new rewards/returns
Let’s collect STATISTICS as evidence
Essential that policies be developed
Let’s imagine a FRAMEWORK
Conclusion
• 1869-2000: A New Paradigm?
– From eliminating the unfits (race issues) to
the cult of efficiency (accounting issues)
– From civilization to economic progress
– From individuals (genius) to organizations
– From men of science to money devoted to
R&D
Conclusion (continued)
• Statistics serve the same ends
– Uses by scientists (and their organizations)
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Increasing the stocks of men of science
Improving social conditions (Cattell)
Scientific recognition (psychologists)
Then, lobbying (US National Science Foundation)
– More human resources (shortages, brain-drain)
– More money to basic research (linear model)
– Uses by governments
• “Control” (by comparisons: scoreboards), but also
support science
• Put science on the political agenda
• Convince people of the value of science
Conclusion (continued)
• The strength of official statistics (as
opposed to scientists’): their regularity
• New indicators to come?
– “Social innovation”
– “Open innovation” (users)
– Creative classes and industries
– Social impacts
• What framework?