Content analysis of seafood news literature
This protocol is extracted from research article:
Leverage points in the financial sector for seafood sustainability
Sci Adv, Oct 2, 2019; DOI: 10.1126/sciadv.aax3324

The content analysis proceeded in two steps and included all FNI monthly issues (2008–2014; n = 84) and UCN online articles (2012–2017; n = 29,865). The limited set of FNI issues constituted a comprehensive, yet small enough, sample to allow an exhaustive manual coding of all articles. On the basis of this review, a codebook with search terms was developed and subsequently applied to the independent set of articles in the UCN archives to narrow down the number of articles to be analyzed in detail. The content analysis was done using MAXQDA 12.3.3.

Codebook development entailed reading FNI articles and conducting exploratory coding of all topics related to the financial sector. Anything relevant to finance was marked to identify three elements: a financial entity (provider of capital), a seafood company (recipient), and a mechanism through which capital flowed from the financial provider to the recipient (fig. S3). A financial entity was defined as an institution that has, or assembles, funds for investment. A seafood company was defined as a company implicated in the seafood value chain, regardless of it being primarily involved in the capture, aquaculture, processing, or retailing sector. Through the exploratory coding, a set of preliminary search terms that were deemed necessary to capture all articles of interest was identified with a focus on financial mechanisms (table S3). Using either recipients or providers as key search terms would have limited the analysis to these specific companies or institutions, and made it impossible to discover any additional types of actors and mechanisms not covered by the FNI archives. Each identified search term was then assessed for relevance by conducting a systematic lexical search of the word within all FNI articles and measuring how many times the term appeared within or outside a coded segment. Words with low relevance, such as “stock” that can refer to financial stock (equity) but was mostly used in relation to fish stock, were not retained (table S3). In step two, the refined list of terms was applied to the entire UCN online archives, and a total of 1246 articles were retrieved using Python version 2.7.13 (table S4). Each article was read in its entirety to identify the type of financial mechanisms and, where available, the recipient and provider. A Jupyter notebook with the data extraction code as well as the list of all articles’ URLs are both available on request.

To summarize the detailed accounts uncovered by the content analysis, and to draw out generalizable insights, we relied on the Weberian notion of “ideal types” to classify firms on the basis of their scale of operations and their ownership structure (publicly listed or privately owned). Ideal types are models, each representing a class (group of objects) with particular characters that can be said to best exemplify the phenomenon in focus—in our case, the intersection of finance with the seafood industry. Using ideal types allows to extend examination beyond the uniqueness of individual cases and develop an understanding of what commonalities exist between cases (55).

Note: The content above has been extracted from a research article, so it may not display correctly.

Please log in to submit your questions online.
Your question will be posted on the Bio-101 website. We will send your questions to the authors of this protocol and Bio-protocol community members who are experienced with this method. you will be informed using the email address associated with your Bio-protocol account.

We use cookies on this site to enhance your user experience. By using our website, you are agreeing to allow the storage of cookies on your computer.