Writers' rooms: theories of contemporary authorship in portraits of creative spaces

This article analyzes two series of photographs and essays on writers’ rooms published in England and Canada in 2007 and 2008. The Guardian’s Writers Rooms series, with photographs by Eamon McCabe, ran in 2007. In the summer of 2008, The Vancouver International Writers and Readers Festival began to post its own version of The Guardian column on its website by displaying, each week leading up to the Festival in September, a different writer’s “writing space” and an accompanying paragraph. I argue that these images of writers’ rooms, which suggest a cultural fascination with authors’ private compositional practices and materials, reveal a great deal about theoretical constructions of authorship implicit in contemporary literary culture. Far from possessing the museum quality of dead authors’ spaces, rooms that are still being used, incorporating new forms of writing technology, and having drafts of manuscripts scattered around them, can offer insight into such well-worn and ineffable areas of speculation as inspiration, singular authorial genius, and literary productivity.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Digital technology and creative arts career patterns in the UK creative economy

The creative industries have attracted the attention of academics and policy makers for the complexity surrounding their development, supply-chains and models of production. In particular, many have recognised the difficulty in capturing the role that digital technologies play within the creative industries. Digital technologies are embedded in the production and market structures of the creative industries and are also partially distinct and discernible from it. This paper unfolds the role played by digital technologies focusing on a key aspect of its development: human capital. Using student micro-data collected by the Higher Education Statistical Agency (HESA) in the United Kingdom, we investigate the characteristics and location determinants of digital graduates. The paper deals specifically with understanding whether digital skills in the UK are equally embedded across the creative industries, or are concentrated in other sub-sectors. Furthermore, it explores the role that these graduates play in each sub-sector and their financial rewards. Findings suggest that digital technology graduates tend to concentrate in the software and gaming sub-sector of the creative industries but also are likely to be in embedded creative jobs outside of the creative industries. Although they are more likely to be in full-time employment than part-time or self-employment, they also suffer from a higher level of unemployment.

Internationalizing the MFA in Creative Writing

is article explores the prospects for internationalizing the Master of Fine Arts (MFA) in Creative Writing, a degree that has gained considerable popularity in the United States in the past half century but has yet to gain much of a foothold in other countries. As part of this exploration, we describe the experiences of estab- lishing the first low-residency Master of Fine Arts in Creative Writing in Asia at City University of Hong Kong, explaining the justification for setting up such a program with reference to the history of teaching creative writing and the current conditions for literary writing in English in Asia and globally. We also reflect upon the processes of planning, curriculum design, and administrative negotiation and that went into setting up the program and report on feedback from the first cohort of students. e experience of setting up this program is used as the basis for raising a number of more general issues regarding the teaching of creative writing in English in interna- tional contexts.

Creative English and public relations in Hong Kong

This paper investigates employer perceptions of the nature and importance of the English language in public relations agencies in Hong Kong. Based on in-depth interviews with senior managers from eight Hong Kong-based public relations companies, it examines how the linguistic currency ofEnglish is used collaboratively in creative organizations. Findings suggest that English is used as the common language among transnational public relations work teams and the clients that they service. Itis also used as a creative resource for facilitating collaboration, staff development and socialization into corporate and professional cultures.

Talent on the move: creative human capital migration patterns in UK

The last decade has seen an increasing number of contributions, from both academics and policy makers, focusing on the role of higher education in developing human capital (Charles, 2003; Cramphorn & Woodlhouse, 1999; Preston & Hammond, 2006) and hence contributing to local and regional growth (Faggian & McCann, 2006; Mathur, 1999; Moretti, 2004). Within this broader literature, the role played by more ‘scientific’ types of human capital, such as STEM (science, technology, engineering, and mathematics) graduates and science parks (Bozeman, Dietz, & Gaughan, 2001; Linderlöf & Löfsten, 2004; Löfsten & Lindelöf, 2005), has also been explored. Little attention has been paid so far, to the role played by more ‘creative’ types of human capital. This chapter aims at filling this gap, in light of the central role that the term ‘creative’ took in policy and academic discourses in the UK (Comunian & Faggian, 2011; Comunian & Gilmore, 2015; DCMS, 2006; Powell, 2007; Universities UK, 2010).

Geography, skills and careers patterns at the boundary of creativity & innovation: digital technology and creative arts graduates in the UK

In the last decades, research on knowledge economies has taken central stage. Within this broader research field, research on the role of digital technologies and the creative industries has become increasingly important for researchers, academics and policy makers with particular focus on their development, supply-chains and models of production. Furthermore, many have recognised that, despite the important role played by digital technologies and innovation in the development of the creative industries, these dynamics are hard to capture and quantify. Digital technologies are embedded in the production and market structures of the creative industries and are also partially distinct and discernible from it. They also seem to play a key role in innovation of access and delivery of creative content. This chapter tries to assess the role played by digital technologies focusing on a key element of their implementation and application: human capital. Using student micro-data collected by the Higher Education Statistical Agency (HESA) in the United Kingdom, we explore the characteristics and location patterns of graduates who entered the creative industries, specifically comparing graduates in the creative arts and graduates from digital technology subjects. We highlight patterns of geographical specialisation but also how different context are able to better integrate creativity and innovation in their workforce. The chapter deals specifically with understanding whether these skills are uniformly embedded across the creative sector or are concentrated in specific sub-sectors of the creative industries. Furthermore, it explores the role that these graduates play in different sub-sector of the creative economy, their economic rewards and their geographical determinants.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.