Discovering Shakespeare’s Personal Style: Editing and Connoisseurship in the Eighteenth Century

This chapter examines the use of connoisseurial rhetoric by Shakespeare editors and critics over the course of the eighteenth century, beginning with Alexander Pope in 1723–5 and concluding with George Steevens in the 1780s and 1790s. Connoisseurship was originally developed by art critics as a discourse for authenticating paintings and drawings. Beginning with Pope, however, literary editors began to draw upon it as an analogy for representing authorial style. As I shall show through an examination of Steevens’s work in compiling the first chronological catalogue of William Hogarth’s prints and paintings, this convergence between art criticism and textual criticism involved more than a simple exchange of metaphors. Connoisseurship offered critics such as Steevens new ways of looking at artworks and assessing their genuineness, modes of vision that could be applied as readily to plays as to paintings. The eighteenth-century art market relied upon the expertise of the connoisseur, who could guarantee that a given painting stemmed from the hand of a particular master. Shakespeare publishing in the eighteenth century likewise came to depend on the expertise of the editor, who could reliably identify Shakespeare’s personal style and distinguish the genuine from the spurious

PREDICTING MUSIC GENRE PREFERENCES BASED ON ONLINE COMMENTS

Communication Accommodation Theory (CAT) states that individuals adapt to each other’s communicative behaviors. This adaptation is called “convergence.” In this work we explore the convergence of writing styles of users of the online music distribution plat- form SoundCloud.com. In order to evaluate our system we created a corpus of over 38,000 comments retrieved from SoundCloud in April 2014. The corpus represents comments from 8 distinct musical genres: Classical, Electronic, Hip Hop, Jazz, Country, Metal, Folk, and World. Our corpus contains: short comments, frequent misspellings, little sentence struc- ture, hashtags, emoticons, and URLs. We adapt techniques used by researchers analyzing other short web-text corpora in order to deal with these problems. We use a supervised machine learning approach to classify the genre of comments in our corpus. We examine the effects of different feature sets and supervised machine learning algorithms on classification accuracy. In total we ran 180 experiments in which we varied: number of genres, feature set composition, and machine learning algorithm. In experiments with all 8 genres we achieve up to 40% accuracy using either a Naive Bayes classifier or C4.5 based classifier with a feature set consisting of 1262 token unigrams and bigrams. This represents a 3 time improvement over chance levels

Recognizability of Individual Creative Style Within and Across Domains: Preliminary Studies

It is hypothesized that creativity arises from the self-mending capacity of an internal model of the world, or worldview. The uniquely honed worldview of a creative individual results in a distinctive style that is recognizable within and across domains. It is further hypothesized that creativity is domaingeneral in the sense that there exist multiple avenues by which the distinctiveness of one’s worldview can be expressed. These hypotheses were tested using art students and creative writing students. Art students guessed significantly above chance both which painting was done by which of five famous artists, and which artwork was done by which of their peers. Similarly, creative writing students guessed significantly above chance both which passage was written by which of five famous writers, and which passage was written by which of their peers. These findings support the hypothesis that creative style is recognizable. Moreover, creative writing students guessed significantly above chance which of their peers produced particular works of art, supporting the hypothesis that creative style is recognizable not just within but across domains

Respectability, morality and disgust in the night-time economy: exploring reactions to ‘lap dance’clubs in England and Wales

The night-time economy is often described as repelling consumers fearful of the ‘undesirable Others’ imagined dominant within such time-spaces. In this paper we explore this by describing attitudes towards, and reactions to, one particularly con- tentious site: the ‘lap dance’ club. Often targeted by campaigners in England and Wales as a source of criminality and anti-sociality, in this paper we shift the focus from fear to disgust, and argue that Sexual Entertainment Venues (SEVs) are opposed on the basis of moral judgments that reflect distinctions of both class and gender. Drawing on documentary analysis, survey results and interview data collected during guided walks, we detail the concerns voiced by those anxious about the presence of lap dance or striptease clubs in their town or city, particularly the notion that they ‘lower the tone’ of particular streets or neighbourhoods. Our conclusion is that the opposition expressed to lap dance clubs is part of an attempt to police the bound- aries of respectable masculinities and femininities, marginalizing the producers and consumers of sexual entertainment through ‘speech acts’ which identify such enter- tainment as unruly, vulgar and uncivilized. These findings are considered in the light of ongoing debates concerning the relations of morality, respectability and disgust

Native Artists: Livelihoods, Resources, Space, Gifts

Examines the experiences of Ojibwe artists in Minnesota, including access to training, funding, space, paying markets, and institutional support; discrimination and isolation; and relationships with communities. Profiles artists and makes recommendations

Mining and Analyzing the Academic Network

Social Network research has attracted the interests of many researchers, not only in analyzing the online social networking applications, such as Facebook and Twitter, but also in providing comprehensive services in scientific research domain. We define an Academic Network as a social network which integrates scientific factors, such as authors, papers, affiliations, publishing venues, and their relationships, such as co-authorship among authors and citations among papers. By mining and analyzing the academic network, we can provide users comprehensive services as searching for research experts, published papers, conferences, as well as detecting research communities or the evolutions hot research topics. We can also provide recommendations to users on with whom to collaborate, whom to cite and where to submit.In this dissertation, we investigate two main tasks that have fundamental applications in the academic network research. In the first, we address the problem of expertise retrieval, also known as expert finding or ranking, in which we identify and return a ranked list of researchers, based upon their estimated expertise or reputation, to user-specified queries. In the second, we address the problem of research action recommendation (prediction), specifically, the tasks of publishing venue recommendation, citation recommendation and coauthor recommendation. For both tasks, to effectively mine and integrate heterogeneous information and therefore develop well-functioning ranking or recommender systems is our principal goal. For the task of expertise retrieval, we first proposed or applied three modified versions of PageRank-like algorithms into citation network analysis; we then proposed an enhanced author-topic model by simultaneously modeling citation and publishing venue information; we finally incorporated the pair-wise learning-to-rank algorithm into traditional topic modeling process, and further improved the model by integrating groups of author-specific features. For the task of research action recommendation, we first proposed an improved neighborhood-based collaborative filtering approach for publishing venue recommendation; we then applied our proposed enhanced author-topic model and demonstrated its effectiveness in both cited author prediction and publishing venue prediction; finally we proposed an extended latent factor model that can jointly model several relations in an academic environment in a unified way and verified its performance in four recommendation tasks: the recommendation on author-co-authorship, author-paper citation, paper-paper citation and paper-venue submission. Extensive experiments conducted on large-scale real-world data sets demonstrated the superiority of our proposed models over other existing state-of-the-art methods

Challenges faced by lecturers in handling large classes

This case study employed qualitative methods to collect data using open- ended questionnaires on a census sample of 38 junior lecturers and face-to-face interviews on seven purposively selected senior lecturers in the School ofSchool of Entrepreneurship and Business Sciences at Chinhoyi University of Technology in Zimbabwe.Data were tabulated, analysed presented narratively using emerging themes permeating the study.The study found that class size does matter as it affects the performance and quality of student learning. Hence, large classes correlate with low student performance. The study recommends that if institutions of higher learning continue recruiting mass students then they should likewise recruit enough academic staff to deal with the large number of students. The conducive learning environments so created would in turn positively affect the academic performance of the students through active involvement of the learners with their content thereby higher order cognitive abilities of problem solving and critical thinking so characteristic of deep learning

ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing

Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot studies where we find that (i) GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly, OpenAssistant, StableLM), and (ii) prompting with a specific question (e.g., to identify errors) outperforms prompting to simply write a review. With these insights, we study the use of LLMs (specifically, GPT-4) for three tasks: 1. Identifying errors: We construct 13 short computer science papers each with a deliberately inserted error, and ask the LLM to check for the correctness of these papers. We observe that the LLM finds errors in 7 of them, spanning both mathematical and conceptual errors. 2. Verifying checklists: We task the LLM to verify 16 closed-ended checklist questions in the respective sections of 15 NeurIPS 2022 papers. We find that across 119 {checklist question, paper} pairs, the LLM had an 86.6% accuracy. 3. Choosing the "better" paper: We generate 10 pairs of abstracts, deliberately designing each pair in such a way that one abstract was clearly superior than the other. The LLM, however, struggled to discern these relatively straightforward distinctions accurately, committing errors in its evaluations for 6 out of the 10 pairs. Based on these experiments, we think that LLMs have a promising use as reviewing assistants for specific reviewing tasks, but not (yet) for complete evaluations of papers or proposals