Benoît Godin

The Linear Model of Innovation The Historical Construction of an Analytical Framework

One of the first (conceptual) frameworks developed for understanding the relation of science and technology to the economy has been the linear model of innovation. The model postulated that innovation starts with basic research, is followed by applied research and development, and ends with production and diffusion. The precise source of the model remains nebulous, having never been documented. Several authors who have used, improved, or criticized the model in the past fifty years rarely acknowledged or cited any original source. The model usually was taken for granted. According to others, however, it comes directly from V. Bush’s Science: The Endless Frontier ([1945] 1995). This article traces the history of the linear model, suggesting that it developed in three steps corresponding to three scientific communities looking at science analytically. The article argues that statistics is a main reason the model is still alive despite criticisms, alternatives, and having been proclaimed dead.

Research and the practice of publication in industries

Abstract Industry publishes relatively few scientific papers. Consequently, it is usually believed that bibliometrics is not very well suited to measure industrial science. The present article tries to assess the usefulness of bibliometrics for measuring industrial scientific activities. 11 814 papers and 84 658 patents originating from 199 multinationals are statistically analyzed in order to understand (1) the importance of industrial publications, (2) the fields of science privileged, (3) the level of science useful to industry, and (4) the science-technology relationships.

Impact of collaborative research on academic science

Over the past 15 years, we have witnessed, according to some analysts, a trend toward greater heterogeneity in scientific research and a growing affiliation of university researchers with extra-university partners. To this end, governments have actively promoted through diverse policy mechanisms greater collaboration and exchange between universities, businesses and governments. This paper assesses the extent to which collaborative research in Canada influences the nature of scientific production and the level of international scientific collaboration. Beliefs that collaborative research is detrimental to academic research do not seem empirically grounded. However the situation must continue to be monitored. Copyright , Beech Tree Publishing.

On the origins of bibliometrics

SummaryAmong the many statistics on science, called scientometrics, bibliometrics holds a privileged place. Bibliometrics is one of the few subfields concerned with measuring the output side of science. According to most “histories”, bibliometrics owes its systematic development mainly to D.J.D. Price and Eugene Garfield, as founders. The few works conducted before the 1950s are usually relegated to prehistory. This paper documents how the systematic counting of publications originated with psychologists. In the early 1900s, psychologists began collecting statistics on their discipline. Publications came to be counted in addresses, reviews and histories of psychology for several decades. The aim was to contribute to the advancement of psychology. Far from being a negligible output of a prehistoric type, both the volume and the systematicness of these efforts are witnesses to what should be considered as pioneering work, and their authors considered as forerunners to bibliometrics.

What is scientific and technological culture and how is it measured? A multidimensional model:

In the last decade, scientific culture has become a theme much discussed at all levels of public discourse. All scientific and technological policies developed in the last few years in OECD countries have included scientific culture as one of their aims, principles, or objectives. Despite the ubiquity of the term “scientific culture,” there is little agreement on its content. Definitions and understandings of what a scientific culture is vary across countries, groups, and individuals. There is also no consensus on how to measure scientific culture. The present paper addresses the question “what is a scientific culture?”. It presents a multidimensional model wherein scientific culture is defined as having two dimensions: individual and social. It then discusses how the model can be used to define indicators of scientific culture and to understand recent developments regarding the role of scientists in the diffusion of scientific culture.

From Eugenics to Scientometrics Galton, Cattell, and Men of Science

In 1906, James McKeen Cattell, editor of Science, published a directory of men of science. American Men of Science was a collection of biographical sketches of thousands of men of science in the USA and was published periodically. It launched, and was used in, the very first systematic quantitative studies on science. Cattell used two concepts for his statistics: productivity, defined as the number of men of science a nation produces, and performance or merit, defined as scientific contributions to research as judged by peers. These are the two dimensions that still define measurement of scientific productivity today: quantity and quality. This paper analyzes the emergence of statistics on science and the very first uses to which they were put. It argues that the measurement of science emerged out of interest in great men, heredity and eugenics, and the contribution of eminent men to civilization. Among these eminent men were men of science, the population of whom was thought to be in decline and insufficiently appreciated and supported. Statistics on men of science thus came to be collected to document the case, and to contribute to the advancement of science and the scientific profession.

Measuring Science: Is There "Basic Research" Without Statistics?

Basic research is a central concept of science and science policy. This article examines the role statistics played in helping to create the concept and shows how it was in part constructed by statistics to serve social and political agendas. Most of this statistical work was conducted in the United States in the 1940s and 1950s, then standardized by the OECD in the 1960s.

Pushes and Pulls Hi(S)tory of the Demand Pull Model of Innovation

Much has been written about the linear model of innovation. While it may have been the dominant model used to explain technological innovation for decades, alternatives did exist. One such alternative—generally discussed as being the exact opposite of the linear model—is the demand-pull model. Beginning in the 1960s, people from different disciplines started looking at technological innovation from a demand rather than a supply perspective. The theory was that technological innovation is stimulated by market demand rather than by scientific discoveries. However, few traces of the demand-pull model remain in the literature today. This article looks at what happened to the demand-pull model from a historical perspective, at three points in time: birth, crystallization, and death. It suggests that the idea of demand as a factor explaining technological innovation emerged in the 1960s, was formalized into models in the 1970-1980s, then got integrated into “multidimensional” models. From then on, the demand-pull model disappeared from the literature, existing only as an object of the past, like the linear model of innovation.

Research and development: how the ‘D’ got into R&D

This paper traces the history of the concept of research and development (R&D) through 70 years of work on taxonomies and statistics on research. It identifies three stages in the construction of development as a category. First, development was only a series or list of activities without a label, but identified for inclusion in questionnaire responses. Second, development came to be identified as such by way of creating a subcategory of research, alongside basic and applied research. Third, development became a separate category, alongside research. It gave us the acronym we now know and use: R&D. Although it is a category of industrial origins, three factors contributed to the inclusion of development in official definitions of research: organizational, analytical, and political. Copyright , Beech Tree Publishing.

Outline for a History of Science Measurement.

The measurement of science and technology (S&T) is now fifty years old. It owes a large part of its existence to the work of the National Science Foundation and the Organization for Economic Cooperation and Development in the 1950s and 1960s. Given the centrality of S&T statistics in science studies, it is surprising that no history of the measurement exists in the literature. This article outlines such a history. The history is cast in the light of social statistics. Like social statistics, S&T indicators are produced mainly by governments but differ in a number of aspects. First, they have not been developed to control individuals. Second, they have taken shape from the start at the international level. Third, they reflect a consensus among states and their organizations. This article shows that this specificity is due to the sociopolitics that drives S&T measurement.