Matching Items (3)
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- All Subjects: digital humanities
- Creators: Evans, James
- Creators: Goodloe, Oscar Laurence
- Creators: Grumbach, Elizabeth
Description
With the advent of sophisticated computer technology, we increasingly see the use of computational techniques in the study of problems from a variety of disciplines, including the humanities. In a field such as poetry, where classic works are subject to frequent re-analysis over the course of years, decades, or even centuries, there is a certain demand for fresh approaches to familiar tasks, and such breaks from convention may even be necessary for the advancement of the field. Existing quantitative studies of poetry have employed computational techniques in their analyses, however, there remains work to be done with regards to the deployment of deep neural networks on large corpora of poetry to classify portions of the works contained therein based on certain features. While applications of neural networks to social media sites, consumer reviews, and other web-originated data are common within computational linguistics and natural language processing, comparatively little work has been done on the computational analysis of poetry using the same techniques. In this work, I begin to lay out the first steps for the study of poetry using neural networks. Using a convolutional neural network to classify author birth date, I was able to not only extract a non-trivial signal from the data, but also identify the presence of clustering within by-author model accuracy. While definitive conclusions about the cause of this clustering were not reached, investigation of this clustering reveals immense heterogeneity in the traits of accurately classified authors. Further study may unpack this clustering and reveal key insights about how temporal information is encoded in poetry. The study of poetry using neural networks remains very open but exhibits potential to be an interesting and deep area of work.
ContributorsGoodloe, Oscar Laurence (Author) / Nishimura, Joel (Thesis director) / Broatch, Jennifer (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Elizabeth Grumbach, the project manager of the Institute for Humanities Research's Digital Humanities Initiative, shares methodologies and best practices for designing a digital humanities project. The workshop will offer participants an introduction to digital humanities fundamentals, specifically tools and methodologies. Participants explore technologies and platforms that allow scholars of all skills levels to engage with digital humanities methods. Participants will be introduced to a variety of tools (including mapping, visualization, data analytics, and multimedia digital publication platforms), and how and why to choose specific applications, platforms, and tools based on project needs.
ContributorsGrumbach, Elizabeth (Author)
Created2018-09-26
Description
How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing interest with a recent technological breakthrough and a new set of commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent ratio. During the historical and conceptual evolution of gene drives beginning in the 1960s, there have been many innovations and publications. Along with that, gene drive science developed considerable public attention, explosion of new scientists, and variation in the way the topic is discussed. It is now time to look at this new organization of science using a systematic approach to characterize the system that has enabled knowledge to grow in this scientific field. This project breaks new ground in how knowledge advances in genetic engineering science, and how scientists understand what a “gene drive” is through analysis of language, communities, and other social factors. In effect, this research will advance multiple fields and enable a deeper understanding of knowledge and complexity. This project documents patterns of publication, collaborative relationships, linguistic variation, innovation, and knowledge expansion. The results of computational analysis provide an in-depth and complete characterization of the structure, dynamics, and evolution of scientific knowledge found in the gene drive technology. Further, time series analysis of the multiple layers of discourse enabled a diachronic connective mapping of collaborative relationships and tracked linguistic variation and change, highlighting where ambiguous language may appear, improving and creating more cohesive scientific language.
Overall, depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore, characterizing the current technological, methodological, and social contexts that can influence scientific knowledge.
ContributorsOToole, Cody Lane (Author) / Laubichler, Manfred (Thesis advisor) / Collins, James P (Committee member) / Simeone, Michael (Committee member) / Evans, James (Committee member) / Arizona State University (Publisher)
Created2021