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Research Note
Public Understanding of Science in turbulent times III: Deficit to dialogue, champions to critics
Public Understanding of Science 1–12 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0963662514549141 pus.sagepub.com
Melanie Smallman University College London, UK
Abstract As part of the 20th Anniversary of the Public Understanding of Science journal, the journal has been reflecting on how the field and journal have developed. This research note takes a closer look at some of the trends, considering the journal’s 50 most cited papers and using IRaMuTeQ, an open-source computer text analysis technique. The research note presents data that show that the move within public engagement from deficit to dialogue has been followed by a further shift from championing dialogue to criticising its practice. This shift has taken place alongside a continued, but changing, interest in media coverage, surveys and models of public understanding.
Keywords dialogue, IRaMuTeQ, public engagement, science communication, text mining
Recently, the Public Understanding of Science (PUS) journal celebrated its 20th Anniversary with reflections on where the journal (and field) has come from and is going. These reflections have identified various trends in the field. Bauer and Howard (2012) described a change in topics covered, from science in general to a focus on particular areas of science, especially genetics and biotechnology, environment and climate change. They also identified a fall in research on literacy, museums, risk perception and the image of scientists, but a rise in papers on public engagement, science communication, perception studies and forms of activism. Subsequently, Suerdem et al. (2013) carried out a lexicographical analysis of abstracts of every article published in PUS since 1992. They identified a consistent interest in public attitudes and mass media coverage and a change in the theoretic language of the journal, from a basic concern with defining the public understanding of science to discussion of public engagement. Corresponding author: Melanie Smallman, Department of Science and Technology Studies, University College London, Gower Street, London WC1E 6BT, UK. Email: [email protected]
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But Bauer and Howard (2012) found that the whole corpus of PUS journal does not necessarily tell the entire story as some papers have been more influential than others. Would an analysis of the most influential papers tell a different story of the evolution of the field? Furthermore, in linguistics, the abstract and other parts of a research article (introduction, methods, etc.) are seen as separate ‘genres’, fulfilling different ‘communicative purposes’ (Samraj, 2005). For example, Bazerman (1989), studying a 1923 research article by Compton, concludes that the abstract ‘focuses on the outcome of the experiment rather than on the background, formulation of the problem or the experimental design’. Different purposes suggest different vocabularies and messages. The vocabulary of the whole text of PUS papers might tell a different story than the abstracts alone. In particular, it might reveal more about the field’s theoretical framework – the ‘background and formulation of the problem’ issues identified as typically missing from abstracts (Bazerman, 1989). Given the importance of the theoretical framework to the field, this article explores this further, looking at the full texts of the 50 most cited (and therefore most influential) articles. We ask what the PUS literature tells us beyond the ‘topics’ being studied. Which of the ‘topics’ are of most interest and how do they relate to one another? What can we learn from the vocabulary about the theoretical framework within which these topics are considered? Is there evidence that the shift in language that Suerdem et al. (2013) identified, from public understanding to public engagement, reflects a change in discourse or simply a change in terminology?
Methodology The full text of the 50 most cited papers in the PUS journal from 1992 to 2010 was identified using the ‘most cited’ tool on the journal’s website on 13 November 2012. The list of papers included is given in the online appendix (available at http://pus.sagepub.com/content/by/supplemental-data), but includes all of those identified as both cited and impactful by Bauer and Howard (2012). A number of elements were removed as they did not contribute to the substantive material: •• •• •• ••
Section and page headings; Figures and graphs; Acknowledgements and notes; References.
This produced a corpus of more than 400,000 words. Like Suerdem et al. (2013), we used a computer-assisted text analysis technique, which presents the researcher with a simplified pattern of the words making up the text for interpretation. This simplified pattern, or ‘map’ of the text, includes lists of the most significant words grouped into ‘classes’ according to their relationship with one another (i.e. words that most often appear in a sentence together), details of the relationship between the words and classes and between the classes (as chi-squared measures) and significant sentences from the original text. None of these produce clear ‘results’, but instead the researcher uses all of these materials, along with the original text, to build understanding of the discourses and to help identify the most plausible inferences from the data. This is not an automatic process but one of abduction. Specifically, for each class, we drew at least two possible interpretations of the word lists and tested them against the additional data and original text, amending our interpretations and rejecting the least plausible ones. Further details of the steps and statistical analyses involved are given in Bara et al. (2007), Kronberger and Wagner (2000), Mutombo (2013) and Stoneman et al. (2013). In this research note, we present the most plausible interpretive labels for each class. The most significant words, their
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level of association with each class and the graphs of the classifications are given in the online appendix. We used the software IRaMuTeQ,1 which has been developed by Pierre Ratinaud and is analogous to the more established commercial ALCESTE software Suerdem et al. (2013) used. Despite slight differences in the precise nature of the algorithms,2 IRaMuTeQ has been shown to produce results comparable to those of ALCESTE (Ratinaud and Dejean, 2009; Ratinaud and Marchand, 2012). IRaMuTeQ does, however, have some advantages over ALCESTE: it is open source and written in the computer language R, so can be customised to perform particular calculations; it uses less computing power, so can process bigger corpora; it also offers additional functionality, particularly in producing graphical representations of the findings.
Results We wanted to know whether the 50 most cited papers told a story different from that of Suerdem et al.’s look at all the papers published and whether the full text would give more details about the theoretical framework. This presents two variables however – abstract versus full text and all papers versus 50 most cited. To separate these out, we conducted two stages of analysis: 1. Abstracts of the 50 most cited in 1992–2010 versus full text of the 50 most cited in 1992–2010. 2. Full text of the 50 most cited in 1992–2001 and 2002–2010 versus abstract of all papers published in 1992–2001 and 2002–2010 (Suerdem et al.’s analysis).3
Analysis 1: abstracts versus full text The analysis of the abstracts produced four classes and that of the full-text produced five classes – summarised in Table 1 (analysis 1). The full text contained analogous classes to the abstracts, but with one further theme around the contextual approach to science communication and relationship between lay and expert knowledge (Class C2.2). This similarity was expected as the abstracts are a sub-set of the whole text. However, the additional class is important as it appears to reflect the more discursive/theoretical aspects of the papers, which Bazerman’s (1989) work suggested might be missing in the abstracts. Going back to the original text, the vocabulary contributing to class C2.2 (contextual approach) was typically drawn from the introduction, discussion and conclusions sections, supporting the proposal that this class reflects the theoretical framing of work in the field. We therefore felt that the full-text corpus was worthy of further investigation.
Analysis 2: comparison of all papers with 50 most cited Our analysis of the full text of the 50 most cited papers produced three classes in 1992–2001 and four classes in 2002–2010, compared to five classes for each time period found by Suerdem et al. looking at the abstracts of all the papers for the same time periods.The comparison is summarised in Table 1 (analysis 2). The classes produced indicate differences between the themes emerging from all the papers published and the most cited. First, the most cited papers appear to be focused around particular topics, not drawn from the full range of topics covered by the journal. For both time periods, the 50 most cited produced fewer classes (three compared to four in 1992–2001; four compared to five in 2002–2010). There were analogous classes in both the most cited papers and all the papers
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PUS: Public Understanding of Science.
Abstracts of 50 most cited papers (n = 50, 72.96% of 233 segments classed) C1.1 (26%) Media/news coverage of science C1.2 (31%) Public discourse and dialogue on new technologies C1.3 (22%) Links between knowledge and attitudes C1.4 (21%) Models of PUS
Full text of 50 most cited papers (n = 50, 87.59% of 11,522 segments classed) C2.1 (23%) Media/news coverage of science C2.5 (24%) Public discourse and dialogue on bioscience C2.4 (17%) Link between knowledge and attitudes C2.3 (15%) Models of PUS C2.2 (21%) Contextual approach
Analysis 1: comparison of classes from analysis of abstracts of 50 most cited versus full text of 50 most cited
1992–2001 n = 33, 68.23% of 7088 text units classified C5.1 (53%) Public understanding models C5.2 (17%) Public and media discourse C5.3 (30%) Media coverage 2002–2010 n = 17, 76.67% of 4402 text units classed C6.1 (24%) Critique of the PUS model C6.2 (17%) Reflections on public engagement exercises C6.3 (25%) Media coverage of medical biotechnology C6.4 (33%) Surveys of attitudes and knowledge
1992–2001 n = 201, 55% of 900 text units classed C3.1 (42%) Public understanding models C3.2 (10%) Popular science, fiction C3.3 (12%) Environment C3.4 (9%) Attitudes and education C3.5 (27%) Media coverage 2002–2010 n = 264; 75% of 116 text units classed C4.1 (18%) Public engagement, formats C4.2 (10%) Attitudes and its factors C4.3 (15%) Agri-food biotech C4.4 (8%) Medical biotechnology C4.5 (48%) Media framing
Full text of 50 most cited papers
Abstracts of all papers (Suerdem et al 2013)
Analysis 2: comparison of classes produced by all papers with 50 most cited (in two timeframes)
Table 1. Classes produced in the analysis of abstracts and full texts of the 50 most cited compared to the classes found in the analysis of the abstracts of all texts.
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around models of public understanding and attitudes to science. Classes relating to popular science and fiction (C3.2) and attitudes and education (C3.4), found in Suerdem et al.’s analysis of all the papers, were, however, not found in our analysis of the 50 most cited papers. While we found no directly equivalent classes to those on medical biotech (C4.4) and agri-food biotech (C4.3), the vocabulary from these classes appears in our classes relating to media coverage (C5.3) and reflections on public engagement exercises (C6.2). The classes on public engagement events (C4.1 ‘Public engagement formats’ and C6.2 ‘Reflections on public engagement exercises’) show a subtle difference in focus too, with the class drawn from the most cited showing more reflexivity rather than advocacy and description. Both these sets of differences appear to be the result of looking at the full text (i.e. the subject in a wider context), rather than the abstracts alone. Most significantly, a class specifically about the move from public understanding to public engagement (C6.1 Critique of deficit model) was only evident in the analysis of the 50 most cited papers. Again, as this relates to the theoretical framework, this could be due to our inclusion of the whole text. Going back to the original text however, we found that this class is not just drawn from the discussion sections – the vocabulary comes from throughout the papers, including the abstracts. This class appears to be a feature of the most cited papers and indicates the importance of this issue in the field.
A closer look The corpus of the full text of the 50 most cited documents is sufficiently large to allow further breakdowns. We therefore split the corpus into four smaller time periods, each containing 12–14 papers: 1992–1994 (12 papers), 1995–1999 (12 papers), 2000–2002 (12 papers) and 2003–2010 (14 papers).4 We asked the software to analyse the 3000 most significant words. Table 2 shows the classes produced, grouped so that classes of a similar theme are placed together horizontally. These findings, consistent with the previous analysis, reveal further details of how the field has evolved over time: Media coverage of science is of continual interest but has changed in three ways over time. 1. First, interest has moved away from general media structure and patterns onto media handling of specific ‘incidents’, especially relating to genetics and climate change. Earlier classes are typically composed of words that describe general features of media coverage, such as ‘frame’, ‘article’, ‘news’ and ‘source’, while later classes make more mention of specific episodes and content of the article, such as ‘Dolly’, ‘Roslyn’, ‘IPCC’ and ‘summit’. A typical phrase contributing to Class 7.3 (Media Coverage 1992–1994) is he study focused specifically on the images of science promoted in the media via the topics portrayed T more frequently.
While a typical phrase contributing to a later class such as C10.3 (Media Coverage of climate change) would be:
By highlighting scientific claims on the risks of climate change to distant physical and human environments the guardian stimulated a sense of global connectedness and global responsibility in spite of leaning often to the views of the times.
We discuss possible explanations later.
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C8.1 (23%) Public discourse on medical genetics
C7.1 (32%) Models of PUS – knowledge and attitudes C7.2 (18%) Contextual approach
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GM: genetic modification; PUS: Public Understanding of Science.
C8.4 (14%) Participant comments on medical genetics C8.5 (18%) Surveys of attitudes and literacy
C8.2 (20%) Media coverage and public perception of risk/environment C8.3 (26%) Media coverage
C7.3 (50%) Media coverage
1995–1999 (n = 12)
1992–1994 (n = 12)
C9.5 (17%) Surveys of attitudes and literacy
C9.3 (16%) Media coverage and public discourse of climate change C9.4 (18%) Media and public discourse of GM and cloning C9.2 (34%) PUS models – knowledge and attitudes C9.1 (15%) Doing public dialogue
2000–2002 (n = 12)
Table 2. Summary classes produced by analysis of 50 most cited papers in four time periods.
C10.2 (17%) Media coverage and public discourse of stem cells and cloning C10.5 (20%) Public engagement – risk, uncertainty and expert advice C10.4 (24%) Public engagement – critique of practice C10.1 (23%) Surveys of attitudes and literacy
C10.3 (16%) Media coverage of climate change
2003–2010 (n = 14)
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2. At the same time as focusing down on particular cases, later classes consider media coverage in a wider, societal context, bringing in words relating to wider social discourse on these topics – such as ‘public perceptions’, ‘political discourse’ and ‘science politics’. For instance, a typical sentence from C9.4 (Media and public discourse on genetic modification (GM) and cloning) is:
This paper will explore the relationship between quality press coverage and public perception in particular the cultivation of the contrast between desirable biomedical red and undesirable agri food green biotechnology in Britain.
3. Third, interest in media coverage has become more international: Class ‘C7:3 Media Coverage’ (1992–1994) appears to be North American focused, with ‘Los Angeles’, ‘Canadian’, ‘American’ and ‘United States’ as the only place names in the list of significant words; ‘C9:4 Media coverage of GM and cloning’ (2000–2002) lists ‘British’, ‘Australian’ ‘Denmark’ and ‘Canada’; C10.3 ‘Media coverage of climate change’ (2003–2010) lists ‘British’, ‘Italian’, ‘American’ and ‘Bulgarian’. Over time, the classes relating to surveys have moved from a discussion of scientific literacy and attitudes to more focus on the purpose and limitations of surveys. For example, in C8.5 (Surveys of attitudes and literacy 1995–1999), ‘item’, ‘attitude’, ‘literacy’, ‘dimension’, ‘understand’ and ‘measure’ were typical words, whereas C10.1 (Surveys of attitudes and literacy 2003–2010) included ‘deficit’, ‘contextualist’, ‘model’, ‘relationship’, ‘critique’ and ‘hypothesis’. To illustrate further from the original text, typical phrases from C8.5 include:
Therefore we aggregated them into a 27 item measure of scientific understanding cronbachs alpha 0 84 we measured interest in science in several ways direct self report by respondents.
While the following is typical of 10.1:
The simple logic of the deficit model is supported by a good deal of cross national empirical evidence for a robust but not especially strong positive correlation between textbook scientific knowledge and favourability of attitude toward science.
Perhaps most interesting is what this analysis reveals about the evolution of discussions around PUS and the move from deficit to dialogue. In the early time period (1992–1994), C7.1 discusses models of PUS – especially the relationship between knowledge and attitudes. Significant words include ‘knowledge’, ‘public’, ‘understand’ and ‘ignorance’ and a typical sentence is: Miller has measured levels of both scientific attentiveness and scientific literacy among the American public. We have benefited from these independent approaches, but we have also developed our own measures or the nature and extent of scientific understanding.
In 2000–2002 however, the equivalent class (C9.2 Models of PUS) has shifted to discuss the ‘problem’ of the PUS model and to begin to propose more complex ways of accounting for the public’s relationship with science, with the focus on our conceptions of science rather than knowledge. Typical words include ‘uncertainty’, ‘problem’, ‘complex’ and ‘construct’. A typical phrase to illustrate this is:
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Public Understanding of Science In this way recent work in the public understanding of science PUS has inclined analysts to turn attention away from the supposed crises of modernity and to attribute to PUS issues a large role in accounting for public discontents with expertise.
We also find a new class in 2000–2002 (C9.1 Doing public dialogue) discussing the practice of public engagement and dialogue. Typical words include ‘consultation’, ‘exercise’, ‘workshop’ and ‘conference’, and an illustrative sentence would be: The public consultation on the biosciences was an initiative without precedent in the UK in terms of the numbers of people involved in both the qualitative and quantitative research and in terms of the focus on a range of technologies grouped under the heading the biosciences.
In the next time period (2002–2010), we found no class devoted to models of PUS, although some of these vocabularies are found in C10.1 ‘Surveys of attitudes and literacy’. The equivalent ‘doing dialogue’ class has also been replaced by C10.4 ‘Public engagement – Critique of practice’, with typical words, including ‘evaluate’, ‘improve’, ‘outcome’, ‘success’, ‘effectiveness’ and ‘validity’. A typical text segment is: The extent to which the report from the debate could reasonably be said to have had an impact on government indeed the first foundation discussion workshop had already taken place 14 November 2002 before the evaluators received a copy of the steering boards list of objectives and indicators of success.
This change suggests a turn in academic approaches to public dialogue – from a normative role advocating dialogue, towards a more critical stance, discussing why particular examples of public dialogue were or were not effective. Up until this point, public dialogue had been the prescription from academics to policy and science. Now the academics appear to have turned from champion to critic. Further evidence of these trends is revealed by the correspondence analysis, produced by crossing the words and classes in the contingency table and giving a graph of the relationships between words in the classes (Figures 1 to 3). The 25 most related words for each class are shown. Word size reflects their association with that class (chi-squared value) rather than frequency. These graphs show how the vocabulary in the 50 most cited papers has moved from three very separate discourses in the early years (1992–1994) to more interconnected discourses in 1995–1999 and 2000–2002, as discussions of dialogue and models of PUS become central and connected to other discussions about media coverage and surveys. In the final timeframe (2003–2010), however, the new class on public engagement critique appears as a distinct class sharing little vocabulary with the other classes – reflecting the critical turn in public engagement research.
Discussion and conclusion This note set out to build on recent work looking at trends in PUS, further exploring the theoretical framework(s) within which the field operates. We were interested in how this has changed over time and whether there is further evidence of a change in this theoretical underpinning over the life of the journal, from deficit to dialogue, identified by Suerdem et al. Looking at the 50 most cited papers, we also aimed to understand which topics were of most interest/influence and how this compares to those which are most published. Our analysis shows that the most influential papers from the journal’s lifespan are focused around fewer topics than those the journal covers overall. In particular, the themes ‘popular science fiction’ and ‘attitudes and education’ were covered by the journal but did not appear in the most
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Figure 1. Correspondence analysis: (a) 1992–1994 and (b) 1995–1999.
Figure 2. Correspondence analysis: 2000–2002.
PUS: Public Understanding of Science; GM: genetic modification.
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Figure 3. Correspondence analysis: 2003–2010.
cited literature. Arguably, these two themes are those most associated with the deficit model as they refer to one-way communication channels, supporting the suggestion that the field has moved from deficit to dialogue. While there are ‘mainstay topics’ of enduring interest across the life of the journal (media coverage, surveys and models of public understanding), their precise focus has changed over time. First, case studies have come to dominate the literature. We suggest a number of possible explanations for this: 1. The dominance of case studies might be a reflection of a maturing field in which the ‘ground rules’ have been laid down and where we are now seeing these theories tested, illuminated or developed by a series of case studies. 2. Contracts to evaluate particular public engagement activities may now provide a significant source of funding for academic research in the field, generating a steady supply of case studies for publication. 3. Methodological approaches could be limiting research to the case study scale. Moving beyond the case study, learning over-arching lessons from bigger datasets and metaanalyses must be a useful direction for future PUS research. Methodologies such as the computer assisted text analysis (CATA) technique used here are likely to offer considerable potential in developing this direction of study in the future. They could also offer the chance to look at more
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immediate and diverse responses to scientific issues by opening up less traditional sources of social intelligence such as twitter feeds or online discussions. This move to case studies has been coupled with a parallel move to setting many of these studies in a wider context of public discussion of science and technology. Whether it is media studies or attitude surveys, different aspects of PUS research appear to be converging around similar questions about the public’s relationship with science. Most of the topics we have identified also appear to have become more international in focus over time, from a broadly UK-/US-focused start. This is perhaps unsurprising, given the increasing recognition of the role of science in international development and economic growth, as well as the journal’s editorial aim to become more international. The exception to this trend, however, is the classes relating to public dialogue, which have stayed firmly UK/Northern European focused. Arguably, this is the result of dialogue being seen by some in these countries as a ‘solution’ to a perceived national crisis in public confidence in science, after high-profile disagreements about bovine spongiform encephalopathy (BSE) and GM foods. The interesting question now is whether this will remain a Northern European phenomenon or whether it will spread in appeal to other countries or even evolve as a model to reflect conditions elsewhere. Perhaps most importantly, as well as the field’s convergence on a dialogue/science and society model, the discourse around this appears to have changed too. Post 2003, alongside arguments for increased public participation in decisions around science and technology, we have identified a critical discourse around the practice and purpose of dialogue. To some extent, this is not news – Irwin et al. (2012) recently characterised current public engagement research as ‘case study followed by critical assessment’. But our finding adds an important nuance to their argument that criticism ‘lies at the heart of’ public engagement work. If that is so, this analysis suggests that the focus of the criticism has changed from an initial criticism of the perceived link between attitudes and knowledge (coupled with an advocacy of dialogue) to today’s criticism of the way in which dialogue is enacted and used by government and scientific organisations. What is not clear from our study, however, is whether the ‘problem’ that the critique might solve has changed – or indeed whether there was a ‘problem’ in the first place. Finally, besides what the classes found tell us, it is interesting to consider what the classes not found tell us about the field. For instance, themes around the quality of messages, information processing and role of emotions have been identified as important in similar analyses of journals in technical, health and political science communications (Graber, 2005; Kim et al., 2010; Rude, 2009). They would all seem relevant topics for those interested in the public understanding of science, but were not found in any analysis of PUS to date. Their absence is worthy of further discussion elsewhere and appears to point to promising areas of research and cross-disciplinary fertilisation in the future. Acknowledgements I would like thank Steven Miller, Jack Stilgoe and Simon J Lock for their comments and advice, to the anonymous referees for their valuable feedback and to Pierre Ratinaud for developing and sharing the IRaMuTeQ software and for his helpful comments on earlier drafts.
Funding This research was funded through an ESRC doctoral training award.
Notes 1.
More details and the IRaMuTeQ software is available from http://www.iramuteq.org
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2. For further details of the differences between IRaMuTeQ and ALCESTE, see Mutombo (2013). 3. A comparison of abstracts of 50 most cited abstracts with abstracts of all papers for these two time periods might have been the obvious first comparisons to make. The size of the corpus created from the abstracts of the 50 most cited for each time period was too small to carry out the analysis however. 4. The uneven time periods of these breakdowns reflect the uneven time spread of the most cited papers.
References Bara J, Weale A and Bicquelet A (2007) Analysing parliamentary debate with computer assistance. Swiss Political Science Review 13(4): 577–605. Bauer MW and Howard S (2012) Public understanding of science – a peer-review journal for turbulent times. Public Understanding of Science 21(3): 258–267. Bazerman C (1989) Shaping Written Knowledge: Genre and Activity of Experimental Article in Science (Rhetoric of the Human Sciences). Madison, WI: University of Wisconsin Press, 400 pp. Graber DA (2005) Political communication faces the 21st century. Journal of Communication 55(3): 479–507. Irwin A, Jensen TE and Jones KE (2012) The good, the bad and the perfect: Criticizing engagement practice. Social Studies of Science 43(1): 118–135. Kim J-N, Park S-C, Yoo S-W and Shen H (2010) Mapping health communication scholarship: Breadth, depth, and agenda of published research in Health Communication. Health Communication 25(6–7): 487–503. Kronberger N and Wagner W (2000) Keywords in context: Statistical analysis of text features. In: Bauer M and Gaskell G (eds) Qualitative Researching with Text, Image and Sound: A Practical Handbook for Social Research. London: SAGE, pp. 299–317. Mutombo E (2013) A bird’s eye view on the EC environmental policy. In: 1st international conference on public policy, Grenoble. Available at: http://www.icpublicpolicy.org/IMG/pdf/panel17_s1_mutombo. pdf Ratinaud P and Dejean S (2009) IRaMuTeQ: Implémentation de la methode ALCESTE d’analyse de texte dans un logiciel libre [Implementation of the ALCESTE method of text analysis in an open-source software]. Presentation. Available at: http://repere.no-ip.org/Members/pratinaud/mes-documents/articleset-presentations/presentation_mashs2009.pdf (accessed 17 April 2013). Ratinaud P and Marchand P (2012) Application de la methode ALCESTE aux gros corpus et stabilitie des mondes lexicaux analyse du CableGate avec IRAMUTEQ [Application of the ALCESTE method to the large corpus and stabilised lexical worlds or ‘cablegate’, using IRAMUTEQ]. In: Actes des 11eme Journées internationales d’Analyse statistique des Données Textuelles (JADT), Liège, pp. 835–844. Rude CD (2009) Mapping the research questions in technical communication. Journal of Business and Technical Communication 23(2): 174–215. Samraj B (2005) An exploration of a genre set: Research article abstracts and introductions in two disciplines. English for Specific Purposes 24(2): 141–156. Stoneman P, Sturgis P and Allum N (2013) Exploring public discourses about emerging technologies through statistical clustering of open-ended survey questions. Public Understanding of Science 22(7): 850–868. Suerdem A, Bauer MW, Howard S and Ruby L (2013) PUS in turbulent times II – A shifting vocabulary that brokers inter-disciplinary knowledge. Public Understanding of Science 22(1): 2–15.
Author biography Melanie Smallman is a researcher and PhD candidate in the Department of Science and Technology Studies at University College London (UCL). She has spent her professional life on the boundary between science communication and policy practice and research, most recently as founder and Director of Think-Lab. Her research interests focus on public attitudes to new technologies and how these views influence policy.
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PUS0010.1177/0963662514549141Public Understanding of ScienceSmallman
P U S
Research Note
Public Understanding of Science in turbulent times III: Deficit to dialogue, champions to critics
Public Understanding of Science 1–12 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0963662514549141 pus.sagepub.com
Melanie Smallman University College London, UK
Abstract As part of the 20th Anniversary of the Public Understanding of Science journal, the journal has been reflecting on how the field and journal have developed. This research note takes a closer look at some of the trends, considering the journal’s 50 most cited papers and using IRaMuTeQ, an open-source computer text analysis technique. The research note presents data that show that the move within public engagement from deficit to dialogue has been followed by a further shift from championing dialogue to criticising its practice. This shift has taken place alongside a continued, but changing, interest in media coverage, surveys and models of public understanding.
Keywords dialogue, IRaMuTeQ, public engagement, science communication, text mining
Recently, the Public Understanding of Science (PUS) journal celebrated its 20th Anniversary with reflections on where the journal (and field) has come from and is going. These reflections have identified various trends in the field. Bauer and Howard (2012) described a change in topics covered, from science in general to a focus on particular areas of science, especially genetics and biotechnology, environment and climate change. They also identified a fall in research on literacy, museums, risk perception and the image of scientists, but a rise in papers on public engagement, science communication, perception studies and forms of activism. Subsequently, Suerdem et al. (2013) carried out a lexicographical analysis of abstracts of every article published in PUS since 1992. They identified a consistent interest in public attitudes and mass media coverage and a change in the theoretic language of the journal, from a basic concern with defining the public understanding of science to discussion of public engagement. Corresponding author: Melanie Smallman, Department of Science and Technology Studies, University College London, Gower Street, London WC1E 6BT, UK. Email: [email protected]
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But Bauer and Howard (2012) found that the whole corpus of PUS journal does not necessarily tell the entire story as some papers have been more influential than others. Would an analysis of the most influential papers tell a different story of the evolution of the field? Furthermore, in linguistics, the abstract and other parts of a research article (introduction, methods, etc.) are seen as separate ‘genres’, fulfilling different ‘communicative purposes’ (Samraj, 2005). For example, Bazerman (1989), studying a 1923 research article by Compton, concludes that the abstract ‘focuses on the outcome of the experiment rather than on the background, formulation of the problem or the experimental design’. Different purposes suggest different vocabularies and messages. The vocabulary of the whole text of PUS papers might tell a different story than the abstracts alone. In particular, it might reveal more about the field’s theoretical framework – the ‘background and formulation of the problem’ issues identified as typically missing from abstracts (Bazerman, 1989). Given the importance of the theoretical framework to the field, this article explores this further, looking at the full texts of the 50 most cited (and therefore most influential) articles. We ask what the PUS literature tells us beyond the ‘topics’ being studied. Which of the ‘topics’ are of most interest and how do they relate to one another? What can we learn from the vocabulary about the theoretical framework within which these topics are considered? Is there evidence that the shift in language that Suerdem et al. (2013) identified, from public understanding to public engagement, reflects a change in discourse or simply a change in terminology?
Methodology The full text of the 50 most cited papers in the PUS journal from 1992 to 2010 was identified using the ‘most cited’ tool on the journal’s website on 13 November 2012. The list of papers included is given in the online appendix (available at http://pus.sagepub.com/content/by/supplemental-data), but includes all of those identified as both cited and impactful by Bauer and Howard (2012). A number of elements were removed as they did not contribute to the substantive material: •• •• •• ••
Section and page headings; Figures and graphs; Acknowledgements and notes; References.
This produced a corpus of more than 400,000 words. Like Suerdem et al. (2013), we used a computer-assisted text analysis technique, which presents the researcher with a simplified pattern of the words making up the text for interpretation. This simplified pattern, or ‘map’ of the text, includes lists of the most significant words grouped into ‘classes’ according to their relationship with one another (i.e. words that most often appear in a sentence together), details of the relationship between the words and classes and between the classes (as chi-squared measures) and significant sentences from the original text. None of these produce clear ‘results’, but instead the researcher uses all of these materials, along with the original text, to build understanding of the discourses and to help identify the most plausible inferences from the data. This is not an automatic process but one of abduction. Specifically, for each class, we drew at least two possible interpretations of the word lists and tested them against the additional data and original text, amending our interpretations and rejecting the least plausible ones. Further details of the steps and statistical analyses involved are given in Bara et al. (2007), Kronberger and Wagner (2000), Mutombo (2013) and Stoneman et al. (2013). In this research note, we present the most plausible interpretive labels for each class. The most significant words, their
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level of association with each class and the graphs of the classifications are given in the online appendix. We used the software IRaMuTeQ,1 which has been developed by Pierre Ratinaud and is analogous to the more established commercial ALCESTE software Suerdem et al. (2013) used. Despite slight differences in the precise nature of the algorithms,2 IRaMuTeQ has been shown to produce results comparable to those of ALCESTE (Ratinaud and Dejean, 2009; Ratinaud and Marchand, 2012). IRaMuTeQ does, however, have some advantages over ALCESTE: it is open source and written in the computer language R, so can be customised to perform particular calculations; it uses less computing power, so can process bigger corpora; it also offers additional functionality, particularly in producing graphical representations of the findings.
Results We wanted to know whether the 50 most cited papers told a story different from that of Suerdem et al.’s look at all the papers published and whether the full text would give more details about the theoretical framework. This presents two variables however – abstract versus full text and all papers versus 50 most cited. To separate these out, we conducted two stages of analysis: 1. Abstracts of the 50 most cited in 1992–2010 versus full text of the 50 most cited in 1992–2010. 2. Full text of the 50 most cited in 1992–2001 and 2002–2010 versus abstract of all papers published in 1992–2001 and 2002–2010 (Suerdem et al.’s analysis).3
Analysis 1: abstracts versus full text The analysis of the abstracts produced four classes and that of the full-text produced five classes – summarised in Table 1 (analysis 1). The full text contained analogous classes to the abstracts, but with one further theme around the contextual approach to science communication and relationship between lay and expert knowledge (Class C2.2). This similarity was expected as the abstracts are a sub-set of the whole text. However, the additional class is important as it appears to reflect the more discursive/theoretical aspects of the papers, which Bazerman’s (1989) work suggested might be missing in the abstracts. Going back to the original text, the vocabulary contributing to class C2.2 (contextual approach) was typically drawn from the introduction, discussion and conclusions sections, supporting the proposal that this class reflects the theoretical framing of work in the field. We therefore felt that the full-text corpus was worthy of further investigation.
Analysis 2: comparison of all papers with 50 most cited Our analysis of the full text of the 50 most cited papers produced three classes in 1992–2001 and four classes in 2002–2010, compared to five classes for each time period found by Suerdem et al. looking at the abstracts of all the papers for the same time periods.The comparison is summarised in Table 1 (analysis 2). The classes produced indicate differences between the themes emerging from all the papers published and the most cited. First, the most cited papers appear to be focused around particular topics, not drawn from the full range of topics covered by the journal. For both time periods, the 50 most cited produced fewer classes (three compared to four in 1992–2001; four compared to five in 2002–2010). There were analogous classes in both the most cited papers and all the papers
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PUS: Public Understanding of Science.
Abstracts of 50 most cited papers (n = 50, 72.96% of 233 segments classed) C1.1 (26%) Media/news coverage of science C1.2 (31%) Public discourse and dialogue on new technologies C1.3 (22%) Links between knowledge and attitudes C1.4 (21%) Models of PUS
Full text of 50 most cited papers (n = 50, 87.59% of 11,522 segments classed) C2.1 (23%) Media/news coverage of science C2.5 (24%) Public discourse and dialogue on bioscience C2.4 (17%) Link between knowledge and attitudes C2.3 (15%) Models of PUS C2.2 (21%) Contextual approach
Analysis 1: comparison of classes from analysis of abstracts of 50 most cited versus full text of 50 most cited
1992–2001 n = 33, 68.23% of 7088 text units classified C5.1 (53%) Public understanding models C5.2 (17%) Public and media discourse C5.3 (30%) Media coverage 2002–2010 n = 17, 76.67% of 4402 text units classed C6.1 (24%) Critique of the PUS model C6.2 (17%) Reflections on public engagement exercises C6.3 (25%) Media coverage of medical biotechnology C6.4 (33%) Surveys of attitudes and knowledge
1992–2001 n = 201, 55% of 900 text units classed C3.1 (42%) Public understanding models C3.2 (10%) Popular science, fiction C3.3 (12%) Environment C3.4 (9%) Attitudes and education C3.5 (27%) Media coverage 2002–2010 n = 264; 75% of 116 text units classed C4.1 (18%) Public engagement, formats C4.2 (10%) Attitudes and its factors C4.3 (15%) Agri-food biotech C4.4 (8%) Medical biotechnology C4.5 (48%) Media framing
Full text of 50 most cited papers
Abstracts of all papers (Suerdem et al 2013)
Analysis 2: comparison of classes produced by all papers with 50 most cited (in two timeframes)
Table 1. Classes produced in the analysis of abstracts and full texts of the 50 most cited compared to the classes found in the analysis of the abstracts of all texts.
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around models of public understanding and attitudes to science. Classes relating to popular science and fiction (C3.2) and attitudes and education (C3.4), found in Suerdem et al.’s analysis of all the papers, were, however, not found in our analysis of the 50 most cited papers. While we found no directly equivalent classes to those on medical biotech (C4.4) and agri-food biotech (C4.3), the vocabulary from these classes appears in our classes relating to media coverage (C5.3) and reflections on public engagement exercises (C6.2). The classes on public engagement events (C4.1 ‘Public engagement formats’ and C6.2 ‘Reflections on public engagement exercises’) show a subtle difference in focus too, with the class drawn from the most cited showing more reflexivity rather than advocacy and description. Both these sets of differences appear to be the result of looking at the full text (i.e. the subject in a wider context), rather than the abstracts alone. Most significantly, a class specifically about the move from public understanding to public engagement (C6.1 Critique of deficit model) was only evident in the analysis of the 50 most cited papers. Again, as this relates to the theoretical framework, this could be due to our inclusion of the whole text. Going back to the original text however, we found that this class is not just drawn from the discussion sections – the vocabulary comes from throughout the papers, including the abstracts. This class appears to be a feature of the most cited papers and indicates the importance of this issue in the field.
A closer look The corpus of the full text of the 50 most cited documents is sufficiently large to allow further breakdowns. We therefore split the corpus into four smaller time periods, each containing 12–14 papers: 1992–1994 (12 papers), 1995–1999 (12 papers), 2000–2002 (12 papers) and 2003–2010 (14 papers).4 We asked the software to analyse the 3000 most significant words. Table 2 shows the classes produced, grouped so that classes of a similar theme are placed together horizontally. These findings, consistent with the previous analysis, reveal further details of how the field has evolved over time: Media coverage of science is of continual interest but has changed in three ways over time. 1. First, interest has moved away from general media structure and patterns onto media handling of specific ‘incidents’, especially relating to genetics and climate change. Earlier classes are typically composed of words that describe general features of media coverage, such as ‘frame’, ‘article’, ‘news’ and ‘source’, while later classes make more mention of specific episodes and content of the article, such as ‘Dolly’, ‘Roslyn’, ‘IPCC’ and ‘summit’. A typical phrase contributing to Class 7.3 (Media Coverage 1992–1994) is he study focused specifically on the images of science promoted in the media via the topics portrayed T more frequently.
While a typical phrase contributing to a later class such as C10.3 (Media Coverage of climate change) would be:
By highlighting scientific claims on the risks of climate change to distant physical and human environments the guardian stimulated a sense of global connectedness and global responsibility in spite of leaning often to the views of the times.
We discuss possible explanations later.
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C8.1 (23%) Public discourse on medical genetics
C7.1 (32%) Models of PUS – knowledge and attitudes C7.2 (18%) Contextual approach
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GM: genetic modification; PUS: Public Understanding of Science.
C8.4 (14%) Participant comments on medical genetics C8.5 (18%) Surveys of attitudes and literacy
C8.2 (20%) Media coverage and public perception of risk/environment C8.3 (26%) Media coverage
C7.3 (50%) Media coverage
1995–1999 (n = 12)
1992–1994 (n = 12)
C9.5 (17%) Surveys of attitudes and literacy
C9.3 (16%) Media coverage and public discourse of climate change C9.4 (18%) Media and public discourse of GM and cloning C9.2 (34%) PUS models – knowledge and attitudes C9.1 (15%) Doing public dialogue
2000–2002 (n = 12)
Table 2. Summary classes produced by analysis of 50 most cited papers in four time periods.
C10.2 (17%) Media coverage and public discourse of stem cells and cloning C10.5 (20%) Public engagement – risk, uncertainty and expert advice C10.4 (24%) Public engagement – critique of practice C10.1 (23%) Surveys of attitudes and literacy
C10.3 (16%) Media coverage of climate change
2003–2010 (n = 14)
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2. At the same time as focusing down on particular cases, later classes consider media coverage in a wider, societal context, bringing in words relating to wider social discourse on these topics – such as ‘public perceptions’, ‘political discourse’ and ‘science politics’. For instance, a typical sentence from C9.4 (Media and public discourse on genetic modification (GM) and cloning) is:
This paper will explore the relationship between quality press coverage and public perception in particular the cultivation of the contrast between desirable biomedical red and undesirable agri food green biotechnology in Britain.
3. Third, interest in media coverage has become more international: Class ‘C7:3 Media Coverage’ (1992–1994) appears to be North American focused, with ‘Los Angeles’, ‘Canadian’, ‘American’ and ‘United States’ as the only place names in the list of significant words; ‘C9:4 Media coverage of GM and cloning’ (2000–2002) lists ‘British’, ‘Australian’ ‘Denmark’ and ‘Canada’; C10.3 ‘Media coverage of climate change’ (2003–2010) lists ‘British’, ‘Italian’, ‘American’ and ‘Bulgarian’. Over time, the classes relating to surveys have moved from a discussion of scientific literacy and attitudes to more focus on the purpose and limitations of surveys. For example, in C8.5 (Surveys of attitudes and literacy 1995–1999), ‘item’, ‘attitude’, ‘literacy’, ‘dimension’, ‘understand’ and ‘measure’ were typical words, whereas C10.1 (Surveys of attitudes and literacy 2003–2010) included ‘deficit’, ‘contextualist’, ‘model’, ‘relationship’, ‘critique’ and ‘hypothesis’. To illustrate further from the original text, typical phrases from C8.5 include:
Therefore we aggregated them into a 27 item measure of scientific understanding cronbachs alpha 0 84 we measured interest in science in several ways direct self report by respondents.
While the following is typical of 10.1:
The simple logic of the deficit model is supported by a good deal of cross national empirical evidence for a robust but not especially strong positive correlation between textbook scientific knowledge and favourability of attitude toward science.
Perhaps most interesting is what this analysis reveals about the evolution of discussions around PUS and the move from deficit to dialogue. In the early time period (1992–1994), C7.1 discusses models of PUS – especially the relationship between knowledge and attitudes. Significant words include ‘knowledge’, ‘public’, ‘understand’ and ‘ignorance’ and a typical sentence is: Miller has measured levels of both scientific attentiveness and scientific literacy among the American public. We have benefited from these independent approaches, but we have also developed our own measures or the nature and extent of scientific understanding.
In 2000–2002 however, the equivalent class (C9.2 Models of PUS) has shifted to discuss the ‘problem’ of the PUS model and to begin to propose more complex ways of accounting for the public’s relationship with science, with the focus on our conceptions of science rather than knowledge. Typical words include ‘uncertainty’, ‘problem’, ‘complex’ and ‘construct’. A typical phrase to illustrate this is:
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Public Understanding of Science In this way recent work in the public understanding of science PUS has inclined analysts to turn attention away from the supposed crises of modernity and to attribute to PUS issues a large role in accounting for public discontents with expertise.
We also find a new class in 2000–2002 (C9.1 Doing public dialogue) discussing the practice of public engagement and dialogue. Typical words include ‘consultation’, ‘exercise’, ‘workshop’ and ‘conference’, and an illustrative sentence would be: The public consultation on the biosciences was an initiative without precedent in the UK in terms of the numbers of people involved in both the qualitative and quantitative research and in terms of the focus on a range of technologies grouped under the heading the biosciences.
In the next time period (2002–2010), we found no class devoted to models of PUS, although some of these vocabularies are found in C10.1 ‘Surveys of attitudes and literacy’. The equivalent ‘doing dialogue’ class has also been replaced by C10.4 ‘Public engagement – Critique of practice’, with typical words, including ‘evaluate’, ‘improve’, ‘outcome’, ‘success’, ‘effectiveness’ and ‘validity’. A typical text segment is: The extent to which the report from the debate could reasonably be said to have had an impact on government indeed the first foundation discussion workshop had already taken place 14 November 2002 before the evaluators received a copy of the steering boards list of objectives and indicators of success.
This change suggests a turn in academic approaches to public dialogue – from a normative role advocating dialogue, towards a more critical stance, discussing why particular examples of public dialogue were or were not effective. Up until this point, public dialogue had been the prescription from academics to policy and science. Now the academics appear to have turned from champion to critic. Further evidence of these trends is revealed by the correspondence analysis, produced by crossing the words and classes in the contingency table and giving a graph of the relationships between words in the classes (Figures 1 to 3). The 25 most related words for each class are shown. Word size reflects their association with that class (chi-squared value) rather than frequency. These graphs show how the vocabulary in the 50 most cited papers has moved from three very separate discourses in the early years (1992–1994) to more interconnected discourses in 1995–1999 and 2000–2002, as discussions of dialogue and models of PUS become central and connected to other discussions about media coverage and surveys. In the final timeframe (2003–2010), however, the new class on public engagement critique appears as a distinct class sharing little vocabulary with the other classes – reflecting the critical turn in public engagement research.
Discussion and conclusion This note set out to build on recent work looking at trends in PUS, further exploring the theoretical framework(s) within which the field operates. We were interested in how this has changed over time and whether there is further evidence of a change in this theoretical underpinning over the life of the journal, from deficit to dialogue, identified by Suerdem et al. Looking at the 50 most cited papers, we also aimed to understand which topics were of most interest/influence and how this compares to those which are most published. Our analysis shows that the most influential papers from the journal’s lifespan are focused around fewer topics than those the journal covers overall. In particular, the themes ‘popular science fiction’ and ‘attitudes and education’ were covered by the journal but did not appear in the most
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Figure 1. Correspondence analysis: (a) 1992–1994 and (b) 1995–1999.
Figure 2. Correspondence analysis: 2000–2002.
PUS: Public Understanding of Science; GM: genetic modification.
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Figure 3. Correspondence analysis: 2003–2010.
cited literature. Arguably, these two themes are those most associated with the deficit model as they refer to one-way communication channels, supporting the suggestion that the field has moved from deficit to dialogue. While there are ‘mainstay topics’ of enduring interest across the life of the journal (media coverage, surveys and models of public understanding), their precise focus has changed over time. First, case studies have come to dominate the literature. We suggest a number of possible explanations for this: 1. The dominance of case studies might be a reflection of a maturing field in which the ‘ground rules’ have been laid down and where we are now seeing these theories tested, illuminated or developed by a series of case studies. 2. Contracts to evaluate particular public engagement activities may now provide a significant source of funding for academic research in the field, generating a steady supply of case studies for publication. 3. Methodological approaches could be limiting research to the case study scale. Moving beyond the case study, learning over-arching lessons from bigger datasets and metaanalyses must be a useful direction for future PUS research. Methodologies such as the computer assisted text analysis (CATA) technique used here are likely to offer considerable potential in developing this direction of study in the future. They could also offer the chance to look at more
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immediate and diverse responses to scientific issues by opening up less traditional sources of social intelligence such as twitter feeds or online discussions. This move to case studies has been coupled with a parallel move to setting many of these studies in a wider context of public discussion of science and technology. Whether it is media studies or attitude surveys, different aspects of PUS research appear to be converging around similar questions about the public’s relationship with science. Most of the topics we have identified also appear to have become more international in focus over time, from a broadly UK-/US-focused start. This is perhaps unsurprising, given the increasing recognition of the role of science in international development and economic growth, as well as the journal’s editorial aim to become more international. The exception to this trend, however, is the classes relating to public dialogue, which have stayed firmly UK/Northern European focused. Arguably, this is the result of dialogue being seen by some in these countries as a ‘solution’ to a perceived national crisis in public confidence in science, after high-profile disagreements about bovine spongiform encephalopathy (BSE) and GM foods. The interesting question now is whether this will remain a Northern European phenomenon or whether it will spread in appeal to other countries or even evolve as a model to reflect conditions elsewhere. Perhaps most importantly, as well as the field’s convergence on a dialogue/science and society model, the discourse around this appears to have changed too. Post 2003, alongside arguments for increased public participation in decisions around science and technology, we have identified a critical discourse around the practice and purpose of dialogue. To some extent, this is not news – Irwin et al. (2012) recently characterised current public engagement research as ‘case study followed by critical assessment’. But our finding adds an important nuance to their argument that criticism ‘lies at the heart of’ public engagement work. If that is so, this analysis suggests that the focus of the criticism has changed from an initial criticism of the perceived link between attitudes and knowledge (coupled with an advocacy of dialogue) to today’s criticism of the way in which dialogue is enacted and used by government and scientific organisations. What is not clear from our study, however, is whether the ‘problem’ that the critique might solve has changed – or indeed whether there was a ‘problem’ in the first place. Finally, besides what the classes found tell us, it is interesting to consider what the classes not found tell us about the field. For instance, themes around the quality of messages, information processing and role of emotions have been identified as important in similar analyses of journals in technical, health and political science communications (Graber, 2005; Kim et al., 2010; Rude, 2009). They would all seem relevant topics for those interested in the public understanding of science, but were not found in any analysis of PUS to date. Their absence is worthy of further discussion elsewhere and appears to point to promising areas of research and cross-disciplinary fertilisation in the future. Acknowledgements I would like thank Steven Miller, Jack Stilgoe and Simon J Lock for their comments and advice, to the anonymous referees for their valuable feedback and to Pierre Ratinaud for developing and sharing the IRaMuTeQ software and for his helpful comments on earlier drafts.
Funding This research was funded through an ESRC doctoral training award.
Notes 1.
More details and the IRaMuTeQ software is available from http://www.iramuteq.org
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2. For further details of the differences between IRaMuTeQ and ALCESTE, see Mutombo (2013). 3. A comparison of abstracts of 50 most cited abstracts with abstracts of all papers for these two time periods might have been the obvious first comparisons to make. The size of the corpus created from the abstracts of the 50 most cited for each time period was too small to carry out the analysis however. 4. The uneven time periods of these breakdowns reflect the uneven time spread of the most cited papers.
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Author biography Melanie Smallman is a researcher and PhD candidate in the Department of Science and Technology Studies at University College London (UCL). She has spent her professional life on the boundary between science communication and policy practice and research, most recently as founder and Director of Think-Lab. Her research interests focus on public attitudes to new technologies and how these views influence policy.
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