My analysis began with coding all gathered media texts prior to a deeper textual analysis. To test the extent that IBs function as public diplomacy, strategic narrative, or contra-flow in relation to official economic agendas, the coding scheme (see all Appendices) identifies who speaks, for whom, and of what. I adopted and modified Robertson’s (2010, 2014) coding schemes from her analyses of mediated cosmopolitanism and IB economic newscast items. Her (2010) scheme coded news items as domestic, international, or intersectional. Domestic stories focus only on the network’s country of origin. Intersectional stories focus on the relationship between the country of origin and one or more other countries. International stories focus on two or more states that are not the country of origin. In the case of international or intersectional news items, I also coded the non-host states names in the order they appeared inspired by Cohen and Atad’s coding framework (2013).
Robertson’s (2014) scheme emphasizes speakers, and codes them as political, economic, expert, ordinary people, or workers (see Appendices C and D). Political speakers are members of government Bankers and business people/owners represent economic speakers. Experts include individuals such as academics, independent economists, or think tank researchers.
Nonprofessionals are “ordinary people” on the street, market, or in their home, perhaps best represented by the “vox pop” interview. I developed the worker category myself to account for
speakers who were engaged in economic activity but did not have economic decision making power.
I placed speakers into specific categories based on their organizational affiliation shown on screen as well as the role they play as speakers. AJE, for example labeled Martin Lidegaard as “Danish Foreign Minister,” a clear indication of his role in government. Political figures could work at national levels as well as regional levels, say the governor or mayor of states and towns, or supra-national levels, for example IMF President Christine Lagarde. I coded activists as political figures as well as their activity reflects their engagement with political processes. Economics figures, in contrast, were typically labeled with words like “president,” “CEO,” “CFO,” “chairman/woman,” or “founder.” These terms signal that the speaker is in a leading position in an business enterprise. For experts, the organizational affiliations sometimes confused coding. If a speaker was affiliated with a university or a think tank and provided analysis of economic policies or issues then their expert status was relatively clear. However, some speakers in this category could be labeled “Senior Statistician National Bureau of Statistics” of China. Is this person a political or expert figure? They are both, surely, but their role in this specific example is to provide analysis and context, not stump for the government. At some levels this distinction is unsatisfactory because it presumes neutral engagement with information on the part of the speaker. That said, their role on the program is to provide authority of expertise. Networks labeled ordinary people with affiliations like “Seoul resident” or “Hong Kong resident” and even “mother.” Ordinary speakers spoke to the effects of economic issues and policies and their remarks lack the authority of expertise or economic leadership. Finally, I distinguished workers from economic figures as the former, while engaged in economic activity in the form of labor, did not speak as leaders of business enterprises, as CEOs did. Examples of this category include
“sushi deliveryman” and “taxi driver.” While individuals can and do inhabit multiple roles, these categories are not meant to be completely accurate representations of these individuals but rather identify the roles which the speaker plays in IB content.
I coded each speaker’s name, organizational affiliation, gender, nationality, and the network on which they appeared. Of these, nationality was the most difficult as accent, skin color, and name may only dimly reflect a person professed national identity or citizenship status. For political figures, for example, this code was simple; Chinese President Xi Jinping is clearly a citizen of China.17 Several economic or expert figures were also relatively simple given their
high public profile, for example Lego CEO Jorgen Vig Knudstrop is an internationally known businessperson. In cases where I could not find any secondary or primary sourcing affirming the national identity of a speaker I left the variable blank. I also adapted Figenschou’s (2010) coding of AJE newscasts for the location of the story and where the story was reported from i.e. in studio, onsite, studio interview, onsite interview, or some mixture of the four. Studio items occur entirely within the studio as a reading of the news by the anchor. Onsite items may be introduced by an in studio anchor but spend the majority of their time with a correspondent at the location of news events. Onsite items typically included several interviews. As such, studio and onsite interviews are distinguished by their focus on a single interview subject, either in studio or at a different location. Whether a report comes from in the studio or out of it is a reflection both of relative importance given to the story and the degree the viewer can engage with the subjects of the story (Chouliaraki 2006b; Figenschou 2012). Finally, I coded each story for the sources of economic data, for example the UN or the World Bank or central banks like the Federal Reserve, which were often clearly labeled on the screen. If there was no sourcing given for data (which
17 When speakers were from Hong Kong, they were labeled as such rather than Chinese to reflect the dual governing structure present in the territory.
was extremely rare) I noted the presence but not the source of the data. Finally, I coded the main topic of a given story, labeled issue, as an indication of the principle subject of the story. As with newspaper headlines (Henry and Tator 2002), the first sentences of a news item illustrate the broader news topic being discussed. Oil prices, for example, were a common topic during the study period and anchors introduced items with clear verbal indications that oil was the primary topic of the item; for example, “the price of oil continues to decline.” Anchor’s subsequent statements then focus the report; for example, “having an effect on Russia’s economy.” Another way to think of this distinction is that oil prices are the phenomena or cause and the Russian economy is the effect. These basic variables allowed me to discern patterns in each broadcaster’s content and thereby direct my subsequent discourse analysis.