{"id":923,"date":"2020-02-16T18:20:15","date_gmt":"2020-02-16T23:20:15","guid":{"rendered":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/?p=923"},"modified":"2020-02-16T18:20:15","modified_gmt":"2020-02-16T23:20:15","slug":"medievalism","status":"publish","type":"post","link":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/medievalism\/","title":{"rendered":"“Medievalism”"},"content":{"rendered":"

After the difficulty I had with the Web of Science for the Citation Analysis Exercise, I was pleased that the first topic of interest I chose for the Network Analysis Exercise, “medievalism,” generated 482 results from the Web of Science. For this blog post, I will discuss two of the networks I created in VOSviewer using data generated by the Web of Science.<\/p>\n

Network 1<\/strong><\/p>\n

\"\"<\/p>\n

This network is based on text data from the journal articles\u2019 title and abstract fields using binary counting, which only counts whether a term is present or absent in a given document.<\/p>\n

The map that visualizes this network shows that the term \u201cmiddle age\u201d has the highest occurrence among the seventy-seven terms with a minimum occurrence of ten; \u201cmiddle age\u201d has an occurrence of ninety-six. For comparison, the terms with the lowest occurrence are \u201cremembrance,\u201d \u201cmedieval memory,\u201d \u201cperson,\u201d and \u201cmedieval period,\u201d each of which occur the minimum of ten. Interestingly, \u201cremembrance\u201d and \u201cmedieval memory\u201d were the two terms with the highest relevance, at 7.83. The term with the lowest relevance was \u201cpresent,\u201d at 0.06. It also seems worth noting that while \u201cnew medievalism,\u201d \u201cneo medievalism,\u201d and \u201cnineteenth century medievalism\u201d were included among the forty-six out of seventy-seven terms selected by VOSviewer based on relevance, the term \u201cmedievalism\u201d was not.<\/p>\n

I am interested in how VOSviewer uses the data generated by the Web of Science to determine which terms are grouped, or \u201cclustered,\u201d together using color in the network visualization. For example, \u201cBritain\u201d and \u201cGermany\u201d are grouped together using yellow, along with \u201cgreat war,\u201d \u201cwar,\u201d and \u201cfantasy,\u201d but \u201cEngland\u201d and \u201cmodern England\u201d are in the group designated by the color blue. To return to my observation on the term \u201cmedievalism\u201d not being selected as a relevant term, I am also interested in what relationship may exist between a term\u2019s occurrence and its relevance.<\/p>\n

Network 2<\/strong><\/p>\n

\"\"<\/p>\n

This network is based on text data from the journal articles\u2019 title and abstract fields using full counting, which counts all of the occurrences of a term in a given document.<\/p>\n

For this network, I used the same parameters as the first network\u2014text data from the journal articles\u2019 title and abstract fields, selection of terms with a minimum occurrence of ten, and selection of the forty-six most relevant terms\u2014except that, instead of using binary counting, I used full counting. The map of this second network resembles the map of the first network somewhat, though this network\u2019s visualization uses six colors rather than four to group terms together, with the addition of purple and a second shade of blue. The terms included in the first and second networks, created using binary and full counting, respectively, also varied.<\/p>\n

Terms included in both networks <\/strong>(or, terms counted by both binary and full counting) (26\/46 terms):<\/p>\n

analysis
\nauthor
\nBritain
\ndebate
\ndevelopment
\nEngland
\nfantasy
\nGermany
\ngreat war
\nknowledge
\nmedieval memory
\nmodern England
\nnarrative
\nneo medievalism
\nnew medievalism
\nnineteenth century medievalism
\nnovel
\norder
\nperiod
\nperson
\npower
\nrelation
\nremembrance
\nrhetoric
\nstate
\nwar<\/p>\n

Terms included in only the first network<\/strong> (binary counting) (20\/46 terms):<\/p>\n

art
\nconcept
\ncontext
\nessay
\nexample
\nform
\ninfluence
\nmedieval period
\nmemory
\nmiddle age
\nmodernity
\npast
\npresent
\nrelationship
\nreturn
\nrole
\ntext
\ntradition
\nuse
\nway<\/p>\n

Terms included in only the second network<\/strong> (full counting) (20\/46 terms):<\/p>\n

approach
\ncountry
\nfield
\nhand
\nindigenous knowledge
\ninterest
\nJoan
\nlanguage
\nMorris
\nnature
\nnostalgia
\nplace
\nplay
\nresearch
\nromanticism
\nself
\nShakespeare
\nsovereignty
\nSpain
\nviolence<\/p>\n

What can you learn from the bibliometric network you have created?<\/strong><\/p>\n

The bibliometric networks I created and the visualization of these networks using VOSviewer reveals prevalent terms and the connections, or \u201clinks,\u201d among these terms in journal articles that address various aspects and instances of medievalism. While I have concerns about the data provided by the Web of Science, which I addressed in my last blog post and will refer to again later in this post, I found the visualization of this data to be useful in thinking about the ways in which these terms are related and how these terms have been used in scholarship.<\/p>\n

How does your choice of data limit your analysis?<\/strong><\/p>\n

My VOSviewer bibliometric networks were created using the most prevalent terms in the titles and abstracts of journal articles included in the Web of Science\u2019s Basic Search results for \u201cmedievalism,\u201d with the range of the articles\u2019 publication dates determined by Pitt\u2019s subscription. As I mentioned in my last blog post, the restriction of the Web of Science\u2019s searchable publications to academic journals of interest does not account for other publication formats, such as books, essays in edited volumes, and exhibition catalogues, or for material published prior to 1945, which is the earliest year included in Pitt\u2019s Web of Science subscription. Using the Web of Science\u2019s data reproduces its limits in VOSviewer network visualizations.<\/p>\n

How can you structure your data to change your analysis?<\/strong><\/p>\n

The first and second bibliometric networks I created show how the structure of data and the way data are counted can change an analysis. By first creating a map based on text data using binary counting and then creating a map based on the same text data using full counting, I saw differences in the terms included in the networks, the relationships among terms, and the ways in which terms were grouped. I could also have changed my analysis by increasing or decreasing the minimum number of occurrences per term, or by changing my selections in other aspects of the map creation process.<\/p>\n

What models of the academic world do these metrics produce?<\/strong><\/p>\n

The choice and structure of data based on the interests of those developing searchable resources, datasets, and visualizations or other forms of data presentation call into question, for me, at least, the extent to which such metrics should be valued, in general and in hiring, evaluation, and tenure processes in the academic world, especially when such metrics are used uncritically across fields of study, as we have discussed in class. I am having difficulty articulating my thoughts in response to this question, so I will be interested in hearing from others as we continue our discussion in class.<\/p>\n","protected":false},"excerpt":{"rendered":"

After the difficulty I had with the Web of Science for the Citation Analysis Exercise, I was pleased that the first topic of interest I chose for the Network Analysis Exercise, “medievalism,” generated 482 results from the Web of Science. For this blog post, I will discuss two of the networks I created in VOSviewer […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[],"class_list":["post-923","post","type-post","status-publish","format-standard","hentry","category-unit-3b"],"_links":{"self":[{"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/posts\/923","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/comments?post=923"}],"version-history":[{"count":0,"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/posts\/923\/revisions"}],"wp:attachment":[{"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/media?parent=923"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/categories?post=923"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.haa.pitt.edu\/digitalcriticalmethods\/wp-json\/wp\/v2\/tags?post=923"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}