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A Framework for Description and Measurement of National Scientific Wealth with a Case Study on Iran

  • Asadi, Saeid (Department of Information Science and Knowledge Studies Shahed University)
  • Received : 2015.12.06
  • Accepted : 2016.06.06
  • Published : 2016.06.30

Abstract

A sustainable development in science, innovation, and technology requires a balanced distribution of scientific wealth in sub-country regions. This paper addresses the issue of geographical distribution of scientific wealth and its goal is to offer a framework to describe and measure the share of provinces in national scientific wealth. Our proposed model divides the indicators of scientific wealth into two groups, production and the use of scientific wealth. To evaluate this model, the scientific wealth of Iran was studied using recorded data on IRANDOC databases. Rich, average, and poor provinces were identified and the results showed that 70% of the scientific wealth belongs to 20% of the provinces. The findings can facilitate planning for a sustainable science and technology policy.

Keywords

Scientific wealth;Iran;Geographical distribution;Regional development;Science productivity

1. INTRODUCTION

Scientific growth is one aspect of development that demonstrates how competitive each nation is regarding its science and technology achievements. Different indicators have been developed in order to show the scientific and scholarly competency of nations, including what follows: (a) quantity of contribution in production of scholarly literature on a global level, such as what appears in Scimago (Guerrero-Botea & Moya-Anegón, 2012) and Web of Science; (b) innovations and registered patents, especially if registered by international authorities; (c) access to and utilization of new technologies such as access to high speed Internet; and (d) the amount of investment in technology and research, especially compared to the whole of expenditures in a single country. Details on science and technology indicators on a national level can be found in Grupp and Mogee (2005). 

Research and development (R&D) intensity refers to the expenditures on R&D as a proportion of GDP and can indicate the relative amount of investment for generating new knowledge (OECD, 2012). Since the 1990s, in many developing countries governments started adopting new knowledge-based economies, paying attention to R&D strategies, science and technology (S&T) infrastructures, and foreign investments in science, research, and technology (U.S. Census Bureau, 2011). The global pattern on expenditure on R&D shows a 6.7 percent increase each year during the first decade of the 21st century; though 2011 statistics revealed that the main countries spending on research and science are limited to North America, Europe, and East Asia. In contrast, the countries in Central America, South America, Central Asia, the Middle East, Australia/Oceania, and Africa have accounted for only 10 percent of global expenditures on R&D in 2011 (International Comparisons of R&D Performance, 2014). Regarding international movements for scientific development, the developing country of Iran has included science and technology development policies in its socioeconomic plans such as the continuing FiveYear Social and Economic Development Plan (PMO, 2003). Paying more attention to science, technology, and innovation as well as increases in higher education has resulted in a rapid increase in the number of Iranian domestic and international publications. For example, Iran has been recognized as the third fastest country in the world in growth of submitting scholarly papers to Web of Science during the period of 2005-2010 (Thomson Reuters, 2012).

In contrast to the quantitative growth of scientific output, there is little evidence about the global scientific impact of Iran. On the other hand, the effects of research activities have not been evident and visible in the social and economic development of the country. From the geographical viewpoint, the unbalanced regional development is an obvious issue in this developing country. 

Focusing on the contribution of different provinces of Iran toward the production and use of scholarly publications, the goal of this research is to study the geographical distribution of the scientific wealth in Iran. The main problem addressed in this research is how to calculate and measure the distribution of the scientific wealth among Iranian provinces. The following questions are studied in this research: 

1. What is the contribution of Iranian provinces in production of national scientific wealth?

2. What is the contribution of Iranian provinces in the use of national scientific wealth?

3. How can the distribution of scientific wealth be measured at a sub-country level?

4. How are the Iranian provinces ranked according to their share in the country’s national scientific wealth?

 

2. A MODEL FOR PRESENTATION OF SCIENTIFIC WEALTH

Science production and use has been a topic of research for years. Inhaber and Alvo (1978) offered an approach to measuring science with paying attention to the inputs and outputs of a scientific activity. The term scientific wealth has appeared in the research entitled “The scientific wealth of nations,” in which the scientific publications of some countries were comparatively studied (May, 1997). The study assessed the scientific wealth of the countries along two items: the number of scientific products and the number of citations. Similar studies have been onducted using other terminologies, especially scientific impact (King, 2004; Belew, 2005; Poddly, 2005; Radicchi, Fortunato, & Castellano, 2008; Lebeau et al., 2008). However, it seems that scientific wealth is something more than the pure counting of scientific publications or citations as many other factors may influence this wealth. Therefore, we propose a more comprehensive model to illustrate the structure of scientific wealth and its components (Fig. 1). In our model, scientific wealth consists of two main categories: science production and use. Each part is di-

 

Fig. 1 Proposed model of the structure of national scientific wealth based on science production and use

 

vided into sub-classes presented as science indicators. 

It is necessary to mention that the presented model can be challenged in two ways: comprehensiveness and reliability. Comprehensiveness means that the model of the scientific wealth should be able to reveal all the intervening aspects of the production and exploitation of the science. Reliability of the presented model also can be found out via common ways such as the feedback of the experts’ view.

According to the amount of the contribution of the provinces in the production and the exploitation of scientific wealth, provinces have been classified into three groups: rich, average, and poor. It can be assumed that provinces with more than n publications are ranked as rich provinces from the viewpoint of the production of scientific wealth. Such thresholds have sometimes been used in other situations. For instance, Iranian families have been divided into two groups, of higher and lower than the poverty line, by the determination of the specific amount of income. We have modified this categorization as it is explained in the Methodology section. Using such categorizations in order to emphasize the inequity of the provinces in the case of science and technology can help to reach a sustainable national development. From a global view, adopting new technology and investments in infrastructures reduces the gap between North and South countries (UNESCO, 2010). 

 

3. LITERATURE REVIEW

Scientometrics researchers have considered geography as a key item for analysis of scientific collaborations. The first steps were taken to illustrate world regions’ contributions in the global citation indices. Frame, Narin, and Carpenter (1977) reported on the global coverage of ISI’s SCI. The rate and inadequate coverage of developing countries’ scientific productions in global citation indices was also considered by Garfield (1983), Moravcsik (1985), Frame (1985), and Shrum (1997). 

Other research efforts show that the international contribution of the different regions and universities of a country follows different patterns. For instance, a study on the international contribution of different organizations and regions of Spain revealed that the older universities have more international contribution. In this country, the Catalonia region also has more international records according to its special autonomy (Olmeda-Gómez et al, 2008). Okubo and Zitt (2004) studied the scientific relationship of France with its neighboring countries and showed that France, Germany, and England had the best level of scientific contribution. From the researchers’ point of view, language has been the key factor in the mentioned international contributions in the way that, for example, there has been more contribution between Finland and Sweden. 

Navaro and Martin (2008) studied the patterns of domestic and international collaboration in some countries. The results show that the more a country produces scientific publications the more it has inner scientific cooperation among its regions and organizations; however, the amount of international contribution is not necessarily high. Instead, the most international collaboration is among countries where their scientific production is not as high. The European countries have paid more attention to scientific relationships with other European countries than for other countries, which probably is the result of geographic proximity.

Glanzel, Schubert, and Czerwon (1999) studied the scientific production of the Europe Union or other world regions. King (2004) studied the publications of 31 countries from the different regions of the world from 1993 to 2000. Osareh and Wilson (2000) focused on the international scientific collaboration of Iranian authors and found out that the most repeated joint papers happened with colleagues from the U.S. and U.K.

Anselin, Varga, and Acs (1997) studied the spatial spillover between university research and high-tech innovations and found spatial externalities between university research and high-tech innovations. Ponds, Oorta, and Frenkena (2007) showed that geographic proximity is important for scientific collaboration of academic-industrial sectors. This proximity is not effective for pure academic relations.

Another geographic feature of scientometrics studies can be found in the visualization of co-authorships around the world. Leydesdorff and Persson (2010), 
Leydesdorff and Rafols (2011), and Bornmann and Leydesdorff (2011) have studied the distribution of science production and scientific effectiveness in the world, with emphasis on Europe and the developed countries. A combination of GIS maps and social network analysis tools can result in interesting representations of knowledge around the world.

Science and technology (S&T) ties with economic development has led to different national and international measurements and indicators. Statistical Abstract of the United States: 2011 contains different tables about the share of U.S. states in national R&D activities (U.S. Census Bureau, 2011). The National Science Board’s Science and Engineering Indicators 2012 reports on the decreasing amount of R&D in national GDP of the U.S. compared to Asian competitors i.e. Japan and South Korea (National Science Board, 2012). More global statistics are available from OECD Scoreboard (OECD, 2012). 

The relationship between scientific outcomes and regional development has been studied by Asadi and Moradi (2014). The correlation between industrial indicators and the scientific productivity of 31 Iranian provinces was examined and the results showed strong correlation.

In summary, the previous work has compared the scientific productivity of different countries or citations among those countries. How the science is nationally distributed has not been carefully studied and this paper focuses on this topic.

 

4. METHODOLOGY

A survey was conducted on available research, science, and technology data on Iran as features of national scientific wealth in order to examine the practicability of the proposed model. Bibliometric techniques such as counting the number of publications co-authorship and citation analysis were used in order to make the components of the suggested model.

The dataset for this research was built using all of the publications indexed in seven databases of IRANDOC,1 which consisted of 504,000. For any specific record in the databases of IRANDOC, there was at least one province affiliated as producer of that publication. Author, Organization, and University fields were looked at to find the producing provinces. For each record, it was possible to find one or more beneficiary provinces, i.e. the geographical entities in Title, Subject, Keywords, and Location fields. The geographic names became uniform and sub-provincial names were replaced with the province name, because research granularity was limited to provinces. For instance, a thesis from the University of Tehran was titled as “Agricultural industries of Shiraz.” The title refers to Shiraz, the provincial capital of Fars province, while it has been researched and written at the University of Tehran. As a result, Tehran province can be considered as the producer and Fars province as the beneficiary for this piece of work.

The number of hits for geographic names was considered as a weight for ranking the Iranian provinces for each single query. Based on the obtained weights, each province was classified in one of these groups: rich, average, and poor. Iran had 31 provinces in 2011 and for each query, these provinces were first looked up in the mentioned fields and then ranked according to the frequency of appearance. Twenty percent of the top and bottom provinces were tagged as rich and poor regions respectively, based on production or beneficiary in the national scientific weights.

 

5. EMPIRICAL RESULTS

The retrieved records from IRANDOC databases have been analyzed in order to get comparative results. Table 1 shows the distribution of scientific products retrieved from the mentioned databases. Tehran province with 77,674 records has the most number of the indexed records. Considering all of the databases, this province still allocates the first position. This is due to the scientific, political, and cultural entrality of Tehran Metropolis, which holds various research centers and large universities. With 18,570 records, Isfahan province is ranked the second productive province. 
Having about 100 cities and towns and locating various centers of higher education, Isfahan province has enough facilities for production of more scientific 
resources. Mazandaran, Fars, Guilan, and Sistan and Baluchistan provinces have been placed in the next rankings. In contrast, Qom, Northern Khorasan, and Alborz provinces had the least scientific products. Due to its new establishment, Alborz province has the least reserved records in the form of Alborz province.

Figure 2 compares the number of retrieved records for 31 provinces regarding the sum of retrieved records from six databases. The share of Tehran province in the retrieved records has obviously been much more than the other provinces – at least 4 times more than Isfahan, the second high ranked province. The overall average of the retrieved resources from the six studied databases is 5,905 titles for each province.

Table 2 shows the results of the scientific products retrieved from each field. The title field, with an average of 2,231 records, has the highest number of location names of Iranian provinces. The field of university with a subtle distance is located in the second ranking with the mean of 2228 records for each province.

Table 3 shows the distribution and percent share of each province from three different aspects related to the scientific wealth of the country. The first and second columns reveal the share of each province as the producer of national scientific wealth. The next two columns indicate the share of each province as the beneficiary of the scientific wealth and the last two columns show the share of the provinces in the total scientific wealth of the country. Tehran province with more than 63% has the highest share and is ranked first for production of national scientific wealth. It also has the first ranking for the use of the scientific wealth. In total, Tehran shares 42% of the national scientific wealth of Iran and is absolutely a unique shareholder. 
Isfahan is the second province after Tehran again in all three aspects. Mazandaran province is the third province and Sistan and Baluchistan is forth in scientific production. The provinces of Fars and Mazandaran are ranked third and fourth in the use of national science. Mazandaran is the third province in the total share of 
scientific wealth of the country and Fars stands fourth. Neighboring Tehran province, the two provinces of Qom and Alborz with less than 500 records are located at the bottom of the list.

Table 4 shows the final rank of 31 provinces based on their share in the national scientific wealth. For instance, Guilan province with 2.8% of the science production has the fifth rank in the science production of the country and is a rich province from this aspect. Having 4.51% of the total records of use, this province is located in the eighth rank and is regarded as an average province; it means that it is neither rich nor poor. With 3.7% of the total, Guilan province is located in the fifth rank and is regarded a rich province regarding 

 

Table 1. Distribution of Retrieved Records for Provinces With Separation of the Database

 

Fig. 2 Frequency of retrieved resources from the 6 databases of IRANDOC for each province

 

its share in the national scientific wealth in total.

Figure 3 shows the status of the rich, average, and poor groups of the provinces respectively from the aspects of production, use, and total share in the scientific wealth of Iran. In the production section, 90% of the scientific products of the country are produced by only 20% of the provinces of the country. From the aspect 
of science use, 20% of the provinces of the country have allocated 56% of the scientific subjects to themselves and in total, the share of the rich provinces from the scientific wealth of the country is 70%, the average provinces is 28%, and the poor provinces is only 2%. On the other hand, six rich scientific provinces of the 
country have allocated 70% of the scientific wealth of Iran to themselves; whereas, the other 25 provinces share only 30% of the scientific wealth of the country. Totally, the findings of the present research show a deep gap between rich provinces and the rest of the provinces from the aspect of the contribution in science production and use.

 

6. DISCUSSION AND CONCLUSIONS

Focusing on the concept of scientific wealth, a novel method was introduced and examined in this research to assess the distribution of scientific wealth at a sub-country level. By having a list of the inputs and outputs of the science cycle, it is possible to assess the amount of the contribution of the regions of a country in production or use of the national scientific wealth. In this research, the amount of the production or use of scientific products was paid attention to as indicators of scientific wealth. More studies are needed to determine the scientific wealth more carefully in each region and all over the country in regard to infrastructure legislation budgets and human resources 

According to the Pareto principle (also known as “the 80-20 rule”) most of the wealth is concentrated in a small proportion of the population (Sanders, 1987). This study revealed that the Pareto principle can be roughly applicable to the share of Iranian provinces in national scientific wealth. It means that a small 20% of Iranian provinces held a 70% share in the national scientific wealth. This can indicate the unbalanced distribution of scientific wealth in Iran, in coordinate with previous research such as Garfield (1983), Moravcsik (1985), Frame (1985), and Shrum (1997) which indicated the scientific production gap between developed and developing countries. Sustainable scientific devel

 

Table 2. Frequency Distribution of Retrieved records for Each Province in Different Fields

 

Table 3. Frequency of Appearance of Provinces as producer or beneficiary in the Dataset

 

Table 4. Ranking the Provinces Based on Indicators of Scientific Wealth

 

Fig. 3 Share of national scientific wealth of Iran among rich, average, and poor provinces

 

opment requires planning for more normal distribution of science in a country. This can be examined for any other country in the world to find out how equally this wealth is distributed.

A careful assessment of the distribution of scientific wealth in the country, the amount of equality, and logical justice in accessing it can be a subject for further research. Besides the quantitative aspect of the scientific productions of a country, the study of the effectiveness of the costs and infrastructure will lead to more useful results. 

 

ACKNOWLEDGEMENTS

This work was approved and supported as a joint research effort by the Iranian Research Institute for Information Science and Technology (IRANDOC) and the Research Deputy of Shahed University. We would like to thank both organizations for their help. 

 

 

 

 

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