CHAPTER 5. Step 2: Insight and the Image Component

2020 ◽  
pp. 76-99
Keyword(s):  
1989 ◽  
Vol 10 (1) ◽  
pp. 140-150 ◽  
Author(s):  
R Cypher ◽  
J.L.C Sanz ◽  
L Snyder

1993 ◽  
Vol 38 (1-5) ◽  
pp. 327-334
Author(s):  
M Maresca ◽  
P Baglietto ◽  
A Giordano

2014 ◽  
Vol 29 (8) ◽  
pp. 466-486 ◽  
Author(s):  
Robert Smith

Purpose – The purpose of this study is to consider entrepreneurial imagery that sheds light on differing and emerging patterns of female entrepreneurial identity which illustrate shifts in the locus of power that challenge masculine hegemony and power structures. As a concept, power has an image component, and shifts in power are often conveyed by subtle changes in the cultural semiotic. Globally, images of female-entrepreneurship are socially constructed using stereotypes which are often pejorative. The semiotics of gendered identity as a complex issue is difficult to measure, assess and understand. Gender has its own semiotic codes, and, universally, images of female-entrepreneurship are socially constructed using pejorative stereotypes. Entrepreneurial imagery can shed light on differing and emerging patterns of female-entrepreneurial identity illustrating shifts in the locus of power that challenge masculine hegemony and power structures. Artefacts, images and semiotics construct alternative gendered social constructs of the entrepreneur to the heroic alpha-male. The imagery associated with the female-entrepreneur is either said to be invisible, or associated with “Pinkness” and the “Pink Ghetto”. Therefore, images, forms and presence associated with gendered entrepreneurial identities have been explored. Design/methodology/approach – One hundred images of female-entrepreneurship were analysed semiotically using photo-montage techniques to identify common stereotypical representations, archetypes and themes. The resultant conceptual typology highlights the existence of near universal, archetypal gendered entrepreneurial stereotypes including the Business Woman; the Matriarch; the Diva; and the Pink-Ghetto Girl. Findings – Although the results are subjective and open to interpretation, they illustrate that the contemporary female-entrepreneur, unlike their male counterparts, is not forced to adopt the persona of the “conforming non-conformist” because they have more options available to them to construct an entrepreneurial identity. Research limitations/implications – This study extends research into entrepreneurial identity by considering visual imagery associated with socially constructed stereotypes. In looking beyond images associated with the “Pink-Ghetto” the author challenges stereotypical representations of the appearance of female-entrepreneurs, what they look like and how they are perceived. Originality/value – This study widens knowledge about entrepreneurship as a socio-economic phenomenon via images forming part of enterprising identity, a physical manifestation of nebulas phenomena acting as “visual metaphors” shaping expected constructs.


1987 ◽  
Vol 15 (1) ◽  
pp. 91-95 ◽  
Author(s):  
Allan Lundy ◽  
Judy A. Rosenberg

The Coopersmith Self-Esteem Inventory and the Rem Sex Role Inventory were administered to 194 adult subjects. It was found that an androgyny scale which emphasized masculinity was most predictive of self-esteem. It was shown that these results were due to the strong independent correlation between masculinity and self-esteem. There were virtually no effects due to femininity, the interaction of femininity and masculinity, or sex. An analysis of the items in the Bem Masculinity Scale suggested that the frequently reported masculinity-self-esteem relationship is an artifact of the inclusion of a “strong self-image” component in the masculine stereotype, despite the fact that this component does not distinguish males from females.


Author(s):  
Nukapeyyi Tanuja

Abstract: Sparse representation(SR) model named convolutional sparsity based morphological component analysis is introduced for pixel-level medical image fusion. The CS-MCA model can achieve multicomponent and global SRs of source images, by integrating MCA and convolutional sparse representation(CSR) into a unified optimization framework. In the existing method, the CSRs of its gradient and texture components are obtained by the CSMCA model using pre-learned dictionaries. Then for each image component, sparse coefficients of all the source images are merged and then fused component is reconstructed using the corresponding dictionary. In the extension mechanism, we are using deep learning based pyramid decomposition. Now a days deep learning is a very demanding technology. Deep learning is used for image classification, object detection, image segmentation, image restoration. Keywords: CNN, CT, MRI, MCA, CS-MCA.


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