The Culture of Numbers:Origins and DevelopmentOf Statistics on Science,Technology and InnovationBENOÎT GODIN

Seminar onResearch and Higher Education PolicyIn Europe and in Czech RepublicPrague27 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)

• Expenditures on research (1962)• Technological balance of payments (1990)• Patents (1994)• Marketed innovation (1992)• High technology products• Productivity (2001)

Accounting (continued)

– Conceptual frameworks• (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?– 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)

• 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?