All posts by erinmullins

Opt-Out

I choose to opt out of this assignment, because (aside from the fact that I have always hated writing anything besides articles and analytical essays) I do not believe that Flarfing has any intrinsic literary value. I do admire the masterful way which Kasey and Gary Sullivan used Flarfing to expose the fraud behind Poetry.com’s contest. The genre of Flarfing, and uncreative writing in general, is useful as an educational tool in discussing what sort of texts have literary value. Yet each singular text lacks literary value. There is nothing to be gained by studying one work of uncreative writing that could not be gained by studying any other work of uncreative writing.

The democratization of language through internet mediums such as AIM, texting, and Twitter has led to a new paradigm of language, in which the reader/listener is expected to understand what is communicated, rather than expecting the author/speaker to communicate effectively. This promotes a lack of restraint and self-reflection.  Additionally this new paradigm encourages appropriation as opposed to production, which retards innovation and original thinking. I believe uncreative writing glorifies this new paradigm, which in my opinion accompanies a decline in language and culture, to which I do not wish to contribute.

Google Books

Because our projects involve well-known works in the public domain, Google Books is a resource applicable to all groups. If you search using Google, web results will include a few from Google Books; thus any person who uses Google Search is already familiar with using Google Books. But Google Search can be limited simply to Books, as well. In addition to searching for our source texts, Google Books allows students to search for other, related texts to investigate.

While the use of Google Books has become ubiquitous through individuals’ use of Google Search, it is important to understand how the service generates results. While the goal of Google Books is “to create a virtual card catalog of all books in all languages”, it is not complete. Books and magazines come from the Partner Program and the Library Project. The Partner Program helps publishers promote their works by providing them for searchable indexing. The Library Project digitizes the collections of partner libraries (including the University of Virginia), allowing users to search many out-of-print texts.

If a text is in the public domain, such as our source texts, then a pdf will be available for download. But if a text is under copyright, varying portions of the text will be available. In some cases a user can browse a few pages at a time; in other cases, only a few snippets as allowed under fair use laws may be available at a time. Additionally, other sources such as Gallica and the Internet Archive may contain texts which Google Books has yet to index. Google Books hopes to digitize all of the approximately 130 million unique books by the end of the decade.

Advanced Google N-Gram Use: Abuse

I first attempted to investigate the prevalence of different types of drug abuse by using the advanced wildcard feature. I entered the search term “*abuse”. However, this yielded the following message: “If you meant to multiply, use parentheses in your search. Wildcards can replace only entire words, not parts of words. Skipping “*abuse”. No valid ngrams to plot!” Believing the ngram unable to plot wildcards which precede the search term, I instead tried a wildcard search using a part-of-speech wildcard dependency. From the search term, “abuse=>*_NOUN”, I was able to generate an ngram.

Still dissatisfied that my initial search did not work, I then decided to search the term, “*_NOUN abuse”, which retains the wildcard aspect of the search, but no longer includes the dependency relationship. This yielded an additional ngram.

The most striking finding from these searches is the fact that searches must follow minutely specific rules. Thus a desired ngram may be difficult to create simply because it must follow a very precise format. Thus, while “*abuse” does not generate an ngram, “*_NOUN abuse” does. Additionally, the two search terms, “*_NOUN abuse” and “abuse=>*_NOUN”, lead to slightly different ngram results. For “abuse=>*_NOUN” the top ten words are substance, child, drug, alcohol, neglect, Child, power, authority, trust, and confidence. For “*_NOUN abuse” the results are substance, child, drug, alcohol, Child, Drug, wife, cocaine, spouse, and men. Unfortunately, wildcard searches cannot be combined with case-insensitive searches, leading to difficulty in understanding the overall prevalence of a term as opposed to the prevalence of its case-variants. And what is the precise difference in meaning between the terms “*_NOUN abuse” and “abuse=>*_NOUN”? Ultimately, advanced usage of the ngram viewer demonstrates that how we search is just as important as what we search.