How to compare the social foundations of science culture: A trial with five cities in Korea

Abstract

Though there have been several indicator systems to monitor the status quo of science and technology and of scientific literacy, few are especially designed for science culture, especially for its social dimension. Furthermore there is little agreement on how to measure it. In a previous study, an indicator system, SCI (Science Culture Indicators), had been developed to monitor the status quo of the science culture of a nation at both individual and social dimensions. The purpose of this study was to explore a practical way to measure and compare local cities’ social foundation of science culture by revising and standardizing the social dimension of SCI and by applying it to five metropolitan cities in Korea. Despite some limits, the results of this study appear not only to reflect the cities’ current situations but also to show the strength and weakness of their social foundation of science culture.

Keywords

city index indicator science culture social foundation

1. Introduction

Over the last three decades, scientific literacy (SL) and public understanding of science (PUS) have been central terms in formal and informal science education (e.g. Durant et al., 1989; DeBoer, 2000; Falk, 2001; Miller, 2004; Bauer et al., 2007). Science culture (SC), frequently used by science communicators, has been also widely used with a meaning similar to that of SL or PUS, but perhaps with a greater emphasis on science in social context (e.g. Godin and Gingras, 2000; Burns et al., 2003). As the informal aspects of science education are getting more attention (e.g. Song and Cho, 2004; Rennie, 2007), and the boundaries between science education, science communication, and PUS are becoming more overlapped (e.g. Stocklmayer et al., 2001; Treise and Weigold, 2002), the idea of SC (or scientific culture) also becomes a main concern of science educators.

Like SL and PUS, SC is rather loosely defined and often allowed various meanings (Godin and Gingras, 2000; Burns et al., 2003). Nevertheless, SC can largely be grouped into the following three types. First, SC can be considered as the features and values uniquely shared within the scientific community. This sense of SC mainly focuses on rationality, logicality, universalism, and others which are supposed to be the culture of the scientific community and vital in the process of generating scientific knowledge (e.g. Jegede, 1997). The scientific culture claimed in The Two Cultures (Snow, 1959) and some process skills in school scientific inquiry (such as, open-mindedness, objectivity, skepticism) would be good examples of this version of SC. Second, SC can be regarded as the processes and products of linking science with society and the public. This version of SC would be personal attitudes, understanding, and values toward science in society and so on, more closely linked with PUS or SL (e.g. Shukla and Bauer, 2009). The third kind of SC is to see science as a part of a culture, implying SC as life modes and social environments related to science and technology (Godin and Gingras, 2000). This version of interpretation, despite its rather broad scope, allows more attention to the social dimension of SC and thus more room for socio-cultural perspectives and multi-faceted aims of science culture and education (e.g. Corrigan et al., 2007; Roth, 2010).

As the importance of SC (including SL and PUS) has been widely recognized, various programs, projects, surveys, and funding for its promotion have also been increasingly developed across the world (e.g. Edwards, 2004: Allum et al., 2008; Turner, 2008). For the promotion of SC, countries often establish new national agencies or utilize existing institutions for more organized support of SC practice: such as the American Association for the Advancement of Science in the US, the British Science Association in the UK, the Chinese Association for Science and Technology in China, the Japan Science and Technology Agency in Japan, and the Korea Foundation for the Advancement of Science and Creativity (formerly known as the Korea Science Foundation) in Korea. This increase of efforts and investments in SC has demanded a need for effective monitoring systems. Along with this need, several science-related indicators (e.g. the National Science Foundation’s Science and Engineering Indicators (SEI) in the US, the European Commission’s Eurobarometer in the EU, Japanese Science and Technology Indicators (STI) in Japan, and Relevance of Science Education (ROSE) in mostly European countries) have been developed to monitor the status quo of science and technology (e.g. NISTEP, 2004; EC, 2005a, 2005b; Jenkins and Pell, 2006; KSF, 2006; NSF, 2006a, 2006b; Schreiner and Sjoberg, 2006). With the exception of ROSE, however, these indicators are largely concerned with statistical data on the national status quo related to science and technology in general, and do not provide specific and systematic data for science culture.

On the other hand, there have been many studies to attempt to define (e.g. DeBoer, 2000; Laugksch, 2000; Roth and Lee, 2002) and to measure (e.g. Laugksch and Spargo, 1996; Miller, 1998, 2004) SL or PUS. However, there has also been criticism that the existing indicators have organizational and academic weaknesses (e.g. Bauer et al., 2000, 2007; Pardo and Calvo, 2002; Kim, 2007). In addition, the aspects checked by some of the above indicators, including ROSE, were mostly limited to the individual person’s dimension of SC, leaving the social dimension of SC untouched (e.g. Shukla and Bauer, 2009). Thus, as claimed by Bauer et al. (2007), there is a need to develop indicators, hopefully a new indicator system, which can be used to monitor and evaluate the status quo SC in society and individuals, and through which the efficacy of SC-related policies, programs, and investments can be both effectively and systemically checked.

In a previous study, we developed an indicator system, called Science Culture Indicators (SCI), which can be used to monitor a nation’s various aspects of personal literacy of science culture (SCI-I: Science Culture Indicators for Individuals) and of social foundation of science culture (SCI-S: Science Culture Indicators for Society) (Song et al., 2008).

Following the SCI framework, the present study aimed to explore a practical way to measure and compare local cities’ social foundations of science culture by revising SCI-S into indicators better suited for monitoring city-level SC, and applying them to five metropolitan cities in Korea to compare those cities’ social foundations. The results are expected to provide information on the strength and weakness of cities’ social foundations of SC, thus to help policy-makers to figure out the priority for future investment and development.

2. Science Culture Indicators (SCI)

Along with the definition by Godin and Gingras (2000: 44) (i.e. “the expression of all the modes through which individuals and society appropriate science and technology”), the main focus of our current study, among a variety of other definitions (e.g. Jegede, 1997; Solomon, 1997; Burns et al., 2003), was a definition of SC as “all modes and values of life which individuals and society share in relation to science and technology.” With this definition of SC, unlike other existing studies (e.g. SEI, Eurobarometer and STI for professional practitioners and/or the general public; ROSE for school science), this study included science-related facets not only of the general public but also of professional practitioners and of school science. The SCI is divided into two dimensions: personal literacy of SC (individual dimension) and social foundation of SC (social dimension). Further, each dimension is divided into two modes: potential and practice. Thus, SCI’s 2 × 2 structure consists of four areas (individual-potential, individual-practice, social-potential, and social-practice). Here the relationships among the areas are of cyclic nature: that is, the potential mode is to be the base of the practice mode, while the latter would lead the change of the former; the individual dimension would be the base of the social dimension, while the latter would be the foundation of the former. Each of those areas is divided into three area-specific categories. As a result, the SCI system has twelve categories (see Table 1). Each category is also further divided into subcategories, and likewise, subcategories into indicators. Each indicator contains items, which are the units of data collection. With this structure, the individual dimension becomes SCI-I (Science Culture Indicators for Individuals), while the social dimension becomes SCI-S (Science Culture Indicators for Society). Since this paper focuses on SCI-S, the explanation of SCI-I can be found elsewhere (Song et al., 2008; Song, 2010).

Table 1.

Dimensions, modes, and categories within SCI.

Dimension	Mode
Dimension		Potential mode	Practice mode
Individual dimension	Opinion	Learning
	Interest	Application
	Understanding	Participation
Social dimension	Human Infrastructure	Event
	Physical Infrastructure	Media
	Institutional Infrastructure	Civil Activity

SCI-S (Science Culture Indicators for Society)

The overall structure of SCI-S is shown in the first three columns of Table 4 (shown later). The social-potential area includes indicators that show the potential capacity of a society to contribute to SC, i.e., indicators for “Human,” “Physical,” and “Institutional” infrastructures for SC. “Human Infrastructure” is a key ingredient in the development of SC with the workforce and education as important factors supporting and developing SC. “Physical Infrastructure” encompasses the facilities for SC. Policies that support individual and societal performances of SC are the main parts of “Institutional Infrastructure.” While physical infrastructure may be a tangible basis of SC, institutional infrastructures are intangible supports for effective performance of facilities and their workforce. Within “Institutional Infrastructure,” the subcategories are based on administrative and financial policies of governments or companies. On the other hand, the social-practice area is divided into “Event,” “Media,” and “Civil Activity.” The “Event” category is related to science events such as science festivals, lectures, and exhibitions in which interactions between science experts and the public would occur. The “Media” category includes categories of media (i.e. print media, television and the internet) covering the science and technology (S&T) field. Finally, within the “Civil Activity” category, the indicators are mainly about scientists’ and the public’s involvements in organized science activities, ranging from public S&T lectures to street demonstrations over socio-scientific issues.

3. Developing a revised SCI-S for cities

Revising SCI-S into an SCI-S for Cities

As SCI-S had been developed to monitor SC’s status quo within a nation, in order to apply it to a city, the indicator system needed to be modified by replacing, removing, or adding indicators based on city-level data availability, resulting in 49 indicators. For example, the indicator “turnover rate of science teachers” had to be removed due to its unavailability of data, while “number of S&T books published annually” was counted from the biggest online bookshop, which was keeping data of regional sales volumes of S&T books, instead of from all publishers in Korea.

To confirm the validity of the SCI-S revisions, we twice consulted with expert groups (11 members for the first and 6 for the second) working within the fields of science education, science communication, and science studies. Among the 11, were 4 university academics, 3 research staff at national institutions, 2 editorial members of a representative popular science magazine, and 2 experienced science teachers; 8 hold doctorate degrees and 3 hold master’s degrees. Regardless of their academic training, all 11 had been actively participating for the past several years in academic, policy-making, and practical activities related to SC. The second group was formed out of the first, in particular from members of the university academic and national institution groups, all of whom were active in SC research activity. The first consultation was mainly focused on the revision of the framework and indicators of SCI-S, while the second was on the processes of weighting of relative importance and standardization of indicators.

Based on those consultations, 3 of 49 proposed indicators were removed owing to low levels of agreement among the experts. In addition, some indicators were amended to become more relevant to the available city-level data. Ultimately, the SCI-S for City was composed of 46 indicators, of which 42 indicators achieved 100 percent agreement, and 4 indicators attained 89 percent agreement on their inclusion by the experts. The complete list of indicators for SCI-S for City is shown in the Online Appendix.

The Human Infrastructure category in the social-potential area consists of four subcategories: “Scientists & Engineers” who participate in S&T activity, “Science Culture Practitioners” whose job is mainly related to science popularization, “Science Teachers” who participate in science education in schools, and “Science Students” who study science and will become participants of SC in the future. Each subcategory consists of measurable workforce indicators related to the status quo of SC in a city. The Physical Infrastructure category consists of three subcategories: “Facilities for R&D” (R&D, research and development), “Facilities for Science Culture,” and “Facilities for Science Education.” Institutional Infrastructure also comprises three subcategories: “Administrative/Financial Policy for R&D,” “Administrative/Financial Policy for Science Culture,” and “Administrative/Financial Policy for Science Education.”

The Event category in the society-practice area consists of two subcategories: “Science Culture Festival and Lecture” and “Science Culture Program.” Each subcategory comprises representative and measurable indicators in the Korean context. The Media category consists of three subcategories: “Print Media,” “Television,” and “Internet.” The Civil Activity category comprises two subcategories: “S&T Specialist Activity” and “General Public Activity.”

As explained earlier, we could not include the annual number of S&T books and magazines published owing to the manner of data accumulation by publishers. Instead, “Print Media” was used and includes the numbers of regular S&T columns in local newspapers, of S&T books annually sold, and of regular subscribers to S&T magazines. Television is represented by the audience-based rating of S&T programs and the number of regular S&T television programs in the city. Since the internet is an especially active medium in Korea, we included “Internet” in the Media category despite the difficulty of determining and collecting appropriate data. The “Internet” subcategory is represented by the number of online memberships of notable S&T websites (in this study, that of KOFAC, http://www.scienceall.com, the science-related website with the largest membership in Korea) and of the subscribers of representative online science newspapers (in this study, The Science Times, the internet newsletter published by KOFAC).

The Civil Activity category comprises two subcategories: “S&T Specialist Activity” and “General Public Activity.” Here, the indicators are the number of memberships in S&T related associations, either specialist (e.g. Korean Physical Society) or of the general public (e.g. Green Korea, a national network of environmentalists), and the number of activities related to socio-scientific issues, such as campaigns, workshops, demonstration assemblies, and so on. Because of its importance as an indicator of voluntary activity, through expert consultation, Civil Activity was given a high relative index of importance despite its small number of indicators.

Indexing of SCI-S for City by the Delphi method

Prior to comparing survey results from the cities, the Delphi method was used to index SCI-S for City by determining the relative importance of areas, categories, subcategories and indicators. Relative importance data were gathered from the opinions of the second group of experts. The process of finding relative importance began from the area level to the subcategory level and then in reverse, and this was repeated until agreement among the expert group was reached. The results of assigning relative importance to SCI-S areas, categories, and subcategories are given in parentheses in Table 4.

The expert group also assigned a relative importance score of high (3 points), medium (2 points), or low (1 point) to each indicator to relatively weight each indicator. The formula to calculate an indicator’s weight was

Indicator’s weight = \frac{Indicator’s average importance score}{Sum of indicator’s average importance score in subcategory}

Subsequently, a score for each indicator, with accuracy to one decimal point and with an interval of 0.5, is determined by multiplying the indicator’s weight by the relative importance of the subcategory, i.e.

Score per Indicator = Indicator’s weight \times Relative importance of subcategory

Information on the scores of indicators in each subcategory is given in the Online Appendix, while examples are presented in Table 2.

Table 2.

Example of method used to determine an indicator’s weight and score per indicator.

Subcategory (relative importance)	Indicator	Indicator’s average relative importance	Indicator’s weight	Score per Indicator
Scientists & engineers (4)	# of scientists & engineers in university	2.3	$\frac{2.3}{2.3 + 2.6 + 2.1} = 0.33$	4*0.33 = 1.32 → 1.5
	# of scientists & engineers in research institute	2.6	$\frac{2.6}{2.3 + 2.6 + 2.1} = 0.37$	4*0.37 = 1.48 → 1.5
	# of scientists & engineers in industry	2.1	$\frac{2.1}{2.3 + 2.6 + 2.1} = 0.30$	4*0.30 = 1.20 → 1

After completing the framework of SCI-S for City, we applied it to five cities in Korea and gathered data from each of those cities. However, in order to create an index system which could be used to compare among cities (or between different periods), standardization of raw data from the cities was needed. For this purpose, we used an HDI (Human Development Index) standardization method, through which standardized data are assigned a value between 0 and 1 (UNDP, 2007/2008). The formula to develop a standardized score from the measured, average, maximum, and minimum values is

Standardized Score = \frac{measured value - minimum}{maximum - minimum}

where the maximum is the largest measured value added to the measured average value, and the minimum is usually zero. Although, like in HDI standardization, the maximum is often calculated based on accumulated data over a given period, since this study did not yet have any accumulated data, the above method of calculating the maximum was adapted from the People Culture Index in Korea (MCST, 2002) which had been also a newly developed index system. Then, city-specific indexes for each indicator were calculated by multiplying the previously obtained score per indicator by the standardized score for each city as

Index per Indicator = Score per Indicator \times Standardized Score

For an example of determining the city-specific indexes, see Table 3.

Table 3.

Example of method of calculating city-specific Index per Indicator.

Indicator	Score per Indicator	Standardized Score		Index per Indicator
Indicator	Score per Indicator	A city	B city	A city	B city
Number of scientists & engineers in university	1.5	0.5	0.3	0.8	0.5

Finally, the total index for each city was calculated by summing all indicator indexes as

Science Culture Index of a City = \sum Index per Indicator

Through this process of indexing, which would give an ideal maximum value of 100, not only can we compare an individual city’s science culture index at a specific time in history, but also, by maintaining data gathering in an appropriate database, we can compare and monitor index changes over a selected period of time.

4. Applying SCI-S for City to five metropolitan cities in Korea

Method

The SCI-S for City and its relative importance and indexing parameters were applied to the five biggest Korean metropolitan cities: Seoul, Daejeon, Gwangju, Daegu, and Busan, each of which is an independent local government and is responsible for its administration, education, and statistics. The capital, Seoul, with a population of 10 million, situated in the northern part of South Korea, is a major political, financial, and cultural center. Daejeon, situated in the middle of the country, has a population of 1.5 million. Considered to be a “city of science,” it encompasses “Daedeok Science Town” with more than 200 research institutions and the National Science Museum. Gwangju, situated in the southwest, has a population of 1.4 million and is regarded as a “city of culture.” Daegu, located in the southeast, has a population of 2.5 million and is referred to as a “city of the textile industry.” Busan is the second largest city in Korea with a population of 3.5 million, is a port city situated on the southeast coast and is famous for its annual film festival.

The data from the cities were gathered between October and December 2008. For data gathering, various statistics and databases at three different levels were used: that is national, regional, and institutional levels. At the national level, databases from the Ministry of Education, Science and Technology (MEST), Korea Foundation for the Advancement of Science and Creativity (KOFAC), and the Korean Federation of Science and Technology Societies (KOFST) were used for the data which are usually kept for national administration purposes: number of scientists and engineers (from MEST), number of visitors to science festivals (from KOFAC), and number of activities about S&T issues (from KOFST). For the region level, data sets from metropolitan government offices and their education offices were mainly used: S&T budget (from metropolitan governments) and number of science teachers (from metropolitan education offices). For the institutional level, data were drawn from related institutions, regional or independent: number of local centers for science culture and programs (institutions in each city), number of S&T regular columns in local newspapers (from each newspaper company), and number of S&T regular television programs (from local cable television companies).

Although much of the needed data could be obtained through the internet, in the forms of databases or annual statistics, many of the data were often not readily available and thus, there was a need for relevant collection, not from existing governmental or metropolitan statistics or databases, but through direct contacts with, and assistance from, related institutions and organizations. Despite laborious efforts of data collection, several indicators were unavailable for various reasons. That is, due to the incomplete analysis of the data (e.g. students’ scholastic performance in science), due to the nature of the database structure (e.g. number of science education researchers), or due to the secret nature of company business (e.g. number of regular subscribers of S&T magazine – only relative ratios according to region were able to be obtained).

Finally, in order to fairly compare cities and to avoid potential influences by the overall size of a city population, according to each indicator’s attribute, data were collected and compared on the basis of different criteria: that is, per city or per million (or thousand) persons. (See the Online Appendix) For example, data for indicators like “number of officials for science culture” and “number of academic institutes for science culture” were counted per city, while data for indicators like “number of scientists and engineers in university” and “number of museums and centers for informal science” were counted per one million persons.

Results

Table 4 shows the overall results of applying SCI-S for City to the five cities, while the Online Appendix provides the raw data for each indictor used to calculate each city’s index score. Out of a possible total score of 100, total SCI-S for City scores were 31.0 for Seoul, 50.4 for Daejeon, 25.9 for Gwangju, 25.0 for Daegu, and 28.9 for Busan. It is important to bear in mind that owing to the unavailability of the data of some indicators the overall scores became smaller than those if all possible data had been obtained. With this limit, in the Potential Mode area, Daejeon obtained the highest score of 27.4 while Daegu had the lowest score of 13.9. In the Practice Mode area, Daejeon again obtained the highest score of 22.9 while Gwangju had the lowest score of 10.6. Not surprisingly, the data for Daejeon, regarded as a “city of science,” indicated its high activity in science culture, at both potential and practice modes among the five cities tested and with the highest overall index. This suggests that provision of a greater potential science foundation can contribute to an increase in the practice of SC activities. In fact, the overall correlation between the potential mode and the practice mode scores for the five cities was 0.95, suggesting that the potential mode would be the determining factor for the practice of SC in society.

Table 4.

Index scores by area, category, and subcategory for five Korean cities.

Area	Category	Subcategory	Seoul	Daejeon	Gwangju	Daegu	Busan
Potential Mode of Social Dimension (55)	Human Infrastructure (20)	Sub-total	5.8	11.7	5.5	5.2	6.5
		Scientists & Engineers (4)	1.3	2.8	0.7	0.5	0.5
		Science Culture Practitioners (7)	1.3	4.7	0.9	1.4	2.9
		Science Teachers (5)	2.3	2.5	2.4	2.1	1.9
		Science Students (4)	0.8	1.7	1.4	1.3	1.1
	Physical Infrastructure (20)	Sub-total	8.5	8.6	5.4	4.0	5.2
		Facilities for R&D (5)	1.6	3.3	1.7	1.1	1.1
		Facilities for Science Culture (9)	4.5	3.5	1.2	0.7	1.6
		Facilities for Science Education (6)	2.3	1.9	2.6	2.2	2.5
	Institutional Infrastructure (15)	Sub-total	3.0	7.1	4.3	4.7	6.4
		Administrative/Financial Policy for R&D (4)	0.7	2.2	0.7	1.1	1.4
		Administrative/Financial Policy for Science Culture (6)	0.5	2.2	2.1	2.1	2.5
		Administrative/Financial Policy for Science Education (5)	1.8	2.7	1.5	1.5	2.5
	Sub-total		17.3	27.4	15.2	13.9	18.1
Practice Mode of Social Dimension (45)	Event (20)	Sub-total	7.1	13.3	3.9	3.6	5.3
		Science Culture Festival and Lecture (9)	2.7	5.9	2.5	2.4	2.1
		Science Culture Program (11)	4.4	7.5	1.4	1.1	3.2
	Media (10)	Sub-total	4.8	4.8	3.8	4.0	3.9
		Print Media (3)	1.4	1.1	0.9	1.1	1.3
		Television (3)	1.4	1.5	1.1	1.4	1.3
		Internet (4)	2.1	2.2	1.7	1.4	1.3
	Civil Activity (15)	Sub-total	1.8	4.8	2.9	3.6	1.6
		S&T Specialist Activity (7)	NA	NA	NA	NA	NA
		General Public Activity (8)	1.8	4.8	3.0	3.6	1.6
	Sub-total		13.7	22.9	10.6	11.1	10.7
Total			31.0	50.4	25.9	25.0	28.9

Among the six categories, Media showed the smallest difference among the cities, which may reflect a situation in which various media practices are pretty similar across the small and densely populated country. On the other hand, the biggest difference was shown in the Event category, which would have a much bigger variance depending on the conditions and commitment of the regions in regard to SC.

The distinctive difference between Daejeon and the rest of the cities was in the Human Infrastructure category, and much of that difference might have contributed to differences in the Event and Civil Activity indicators for Daejeon and other cities. Since Daejeon has a large concentration of scientists and engineers in its Daedeok Science Town, the city has a greater opportunity to host a variety of science-related events and activities with its abundant science-related resources, frequently supported by the government research funding strategy of allocating 3 percent of the labor cost of the fund to be used for PUS and Public Communication of Science and Technology. In addition, the National Science Museum and Expo Science Park in Daejeon are two of the main attractions for students in Korea.

On the other hand, Seoul only obtained moderate index scores across the categories and subcategories. However, Seoul did obtain high scores in Physical Infrastructure, especially in “Facilities for Science Culture.” This may be because Seoul is the site of several research institutions for science communication and science education. Seoul’s Institutional Infrastructure was rather weak, particularly in the subcategory of administrative and financial support for SC. The cities of Gwangju and Daegu received the lowest overall index scores among the five cities assessed. Gwangju, traditionally known as a “city of culture,” obtained low scores in the Event and Media categories. That may reflect the city’s effort to promote artistic cultural activities, rather than scientific activities. For example, the city focuses on the “Gwangju Biennale,” a world-famous international event that emphasizes modern art. This situation may be similar to those in Daegu and Busan which both showed similar results on those indexes. For example, Daegu’s ongoing international event, the “Milano Project,” focuses on textiles and clothing design, while Busan holds the annual “Busan International Film Festival.” Thus, in contrast to Daegeon, which has an abundance of science-related social activities, Gwangju, Daegu, and Busan have their own ongoing, large-scale international events that attract the attention of the cities’ populations, deflecting resources away from science-related events.

5. Discussion

Based on the SCI framework from a previous study, we developed an indicators system that shows cities’ social foundations of SC and applied the new indicator system to five metropolitan cities, thereby checking the new indexes’ applicability. Using the Delphi method and some formulas for weighting the various parameters in the indexing process, we developed the SCI-S for City, which provides concise and suitable indexing, using available city-level data. By converting indicators into indexes, direct and easy comparisons between cities, which would provide information on each city’s SC at a glance, became possible. Applying SCI-S for City to the five Korean cities, we compared the status quo of their SC and found that Daejeon, with highest scores in all categories, was the most advanced in its social foundation of SC. On the basis of these results, we suggest that SCI-S for City could be an efficient and systematic tool which would be useful in comparing SC between cities and possibly between different moments in time in a city. In addition to the identified strength and weakness of each city’s social foundation of SC, the very high correlation (.95) between the potential and the practice modes across the cities hints that (human, physical, and institutional) infrastructures of SC are essential conditions for active practice of SC, although any causal relationship or in-depth understanding of them would require further studies. This high correlation also suggests that the current indicator system could be reduced into a simpler form with a smaller number of indicators.

In this and in previous studies, SC is rather widely defined, with an overall intention to include, as well as PUS, components of professional S&T and school science education. This is based on our belief that these three components are the main SC components of a society. The indicators included in SCI-S for City were those for which data would be available at the city level. However, data of some indicators in the end turned out to be unavailable for various reasons (e.g. the national policy to keep closed the data of regional students’ scholastic performance, the way of collecting data from its member associations by the Korean Federation of Science and Technology Societies). The final results of the comparison among the cities were inevitably affected by this to some extent, and thus these indicators need to be further revised or replaced by better ones. It is also true that the processes undertaken in this study for the Delphi method of relative importance, indexing and counting criteria (i.e. per city or per person) may not be the only or the best way to proceed. With these limits, the indicators and data provided here are neither complete nor optimal, but rather need to be taken as useful, efficient, and exemplary. It is also true that indicators in general may not be universal across nations and cultures.

Despite the limits of its current form, the results of this study appear to reflect well the current situations of and are able to show both the strength and weakness of cities’ foundations of science culture. The results are expected to help in the drafting of guidelines for allocating resources to SC policy-makers and to provide researchers with a good example for similar studies. In addition, through its 2 × 2 structure, the SCI system would allow us to figure out the complex inner relationships of SC, such as correlations among four areas (i.e. SCI-I Potential, SCI-I Practice, SCI-S Potential, and SCI-S Practice) and their categories. Comparisons of SCI-S with other existing science indicator systems (like SEI, STI) or international comparative surveys (like TIMSS, PISA, ROSE) would also give us understandings of various aspects of SC and SC’s relationships with other related factors.

Footnotes

Author biographies

Jinwoong Song is Professor of Science Education at Seoul National University, with research interests in: physics learning and cognition, science education in context, history of science education and informal science education. He is currently exploring effective ways of bridging school science education and informal science education and of monitoring the science culture of society as well as individuals.

Minkyung Chung, Eunjeong Choi, and Leekyoung Kim are science teachers working at secondary schools in Korea. All of them recently finished their MA degree in science education at Seoul National University. For their master’s theses, they carried out researches on different aspects of students’ science culture literacy on the basis of Science Culture Indicators (SCI). After returning to schools, they continue to work to improve students’ science culture literacy.

Sook-Kyoung Cho has been working for Korea Foundation for the Advancement of Science and Creativity (formerly Korea Science Foundation) since 2001. Her doctoral thesis was about the history of the Science Museum of London and the popularization of physical science during the Victorian period. She is currently working on various governmental policies on science communication and science culture and working as a Science Committee member of the Public Communication of Science and Technology (PCST) network. Online Appendix: SCI-S for cities and the data from five metropolitan cities in Korea

References

Allum

Sturgis

Tabourazi

Brunton-Smith

(2008) Science knowledge and attitudes across cultures: A meta-analysis. Public Understanding of Science 17(1): 35–54.

Bauer

Petkova

Boyadjieva

(2000) Public knowledge of and attitudes to science: Alternative measures that may

end the “science war.”

Science, Technology, & Human Values 25(1): 30–51.

Bauer

Allum

Miller

(2007) What can we learn from 25 years of PUS survey research? Liberating and expanding the agenda. Public Understanding of Science 16(1): 79–95.

Burns

O’Connor

Stocklmayer

(2003) Science communication: A contemporary definition. Public Understanding of Science 12(2): 183–202.

Corrigan

Dillon

Gunstone

(eds) (2007) The Re-Emergence of Values in Science Education. Rotterdam: Sense Publishers.

DeBoer

(2000) Scientific literacy: Another look at its historical and contemporary meanings and its relationship to science education reform. Journal of Research in Science Teaching 37(6): 582–601.

Durant

Evans

Thomas

(1989) The public understanding of science. Nature 340: 11–14.

Edwards

(2004) Evaluating European public awareness of science initiatives. Science Communication 25(3): 260–271.

European Commission (EC) (2005a) Special Eurobarometer 224: Europeans, Science and Technology. Brussels: European Commission.

10.

European Commission (EC) (2005b) Special Eurobarometer 225: Social Values, Science and Technology. Brussels: European Commission.

11.

Falk

(2001) Free-Choice Science Education: How We Learn Science Outside of School. New York: Teachers College Press.

12.

Godin

Gingras

(2000) What is scientific and technological culture and how is it measured? A multidimensional model. Public Understanding of Science 9(1): 43–58.

13.

Jegede

(1997) School science and the development of scientific culture: A review of contemporary science education in Africa. International Journal of Science Education 19(1): 1–20.

14.

Jenkins

Pell

(2006) The Relevance of Science Education (ROSE) Project in England: A Summary of Findings. Leeds: Centre for Studies in Science and Mathematics Education, University of Leeds.

15.

Kim

H-S

(2007) PEP/IS: A new model for communicative effectiveness of science. Science Communication 28(3): 287–313.

16.

Korea Science Foundation (KSF) (2006) Report of Public Understanding of Science and Technology. Seoul: Korea Science Foundation.

17.

Laugksch

(2000) Science literacy: A conceptual overview. Science Education 84(1): 71–94.

18.

Laugksch

Spargo

(1996) Construction of a paper-and-pencil Test of Basic Scientific Literacy based on selected literacy goals recommended by the American Association for the Advancement of Science. Public Understanding of Science 5(4): 331–359.

19.

Miller

(1998) The measurement of civic scientific literacy. Public Understanding of Science 7(3): 203–223.

20.

Miller

(2004) Public understanding of, and attitudes toward, scientific research: What we know and what we need to know. Public Understanding of Science 13(3): 273–294.

21.

Ministry of Culture, Sports and Tourism (MCST) (2002) The Development of Cultural Index 2002. Seoul: Ministry of Culture, Sports and Tourism.

22.

National Institute of Science and Technology Policy (NISTEP) (2004) Science and Technology Indicators: 2004, A Systematic Analysis of Science and Technology Activities in Japan. Tokyo: NISTEP.

23.

National Science Foundation (NSF) (2006a) Science and Engineering Indicators 2006: Volume 1. Arlington, VA: National Science Foundation.

24.

National Science Foundation (NSF) (2006b) Science and Engineering Indicators 2006: Volume 2. Arlington, VA: National Science Foundation.

25.

Pardo

Calvo

(2002) Attitudes toward science among the European public: A methodological analysis. Public Understanding of Science 11(2): 155–195.

26.

Rennie

(2007) Learning science outside of school. In: Abell

Lederman

(eds) Handbook of Research on Science Education. Mahwah, NJ: Lawrence Erlbaum Associates, 125–167.

27.

Roth

(ed.) (2010) Re/Structuring Science Education: ReUniting Sociological and Psychological Perspectives. London: Springer.

28.

Roth

Lee

(2002) Scientific literacy as collective praxis. Public Understanding of Science 11(1): 33–56.

29.

Schreiner

Sjoberg

(2006) The Relevance of Science Education (ROSE). Oslo: University of Oslo.

30.

Shukla

Bauer

(2009) Construction and validation of “science culture index.” Results from comparative analysis of engagement, knowledge and attitudes to science: India and Europe. Working Paper No. 100. London: Royal Society.

31.

Snow

(1959) The Two Cultures. Cambridge: Cambridge University Press.

32.

Solomon

(1997) School science and the future of scientific culture. In: Levinson

Thomas

(eds) Science Today: Problem or Crisis? London: Routledge, 151–162.

33.

Song

(2010) Rethinking the context and social foundation of science education. In: Lee

(ed.) Handbook of Research in Science Education in Asia. Rotterdam: Sense Publishers, 155–169.

34.

Song

Cho

(2004) Yet another paradigm shift? From minds-on to hearts-on. Journal of the Korean Association for Science Education 24(1): 129–145.

35.

Song

Choi

Kim

Chung

Lim

Cho

(2008) Developing the indicator system for diagnosing the national status quo of science culture. Journal of Korean Association for Science Education 28(4): 316–330.

36.

Stocklmayer

Gore

Bryant

(eds) (2001) Science Communication in Theory and Practice. Dordrecht: Kluwer Academic Publishers.

37.

Treise

Weigold

(2002) Advancing science communication: A survey of science communicators. Science Communication 23(3): 310–322.

38.

Turner

(2008) School science and its controversies; or whatever happened to scientific literacy? Public Understanding of Science 17(1): 55–72.

39.

UNDP (2007/2008) Human Development Report 2007/2008. New York: United Nations Development Programme.

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