Lifelog using Mobility Context Information in Urban City Area

2019 ◽  
Author(s):  
Yanlei Gu ◽  
Dailin Li ◽  
Yoshihiko Kamiya ◽  
Shunsuke Kamijo
Keyword(s):  
Context Information ◽  
City Area ◽  
Urban City

Integration of positioning and activity context information for lifelog in urban city area

Navigation ◽  
2020 ◽  
Vol 67 (1) ◽  
pp. 163-179
Author(s):  
Yanlei Gu ◽  
Dailin Li ◽  
Yoshihiko Kamiya ◽  
Shunsuke Kamijo
Keyword(s):  
Context Information ◽  
City Area ◽  
Urban City

Entrepreneurial Needs for Outer Urban Co-Working Hubs

2021 ◽  
pp. 190-219
Author(s):  
Emsie Arnoldi ◽  
Vanessa Cooper ◽  
Cathy Greenfield ◽  
Rachelle Bosua ◽  
Huck Ying Ch'ng
Keyword(s):  
Inner City ◽  
Activity Theory ◽  
Urban Areas ◽  
Exploratory Study ◽  
Flexible Work ◽  
City Area ◽  
Fast Growing ◽  
Work Arrangements ◽  
Urban City ◽  
Coworking Spaces

Workspaces and workplaces have changed significantly over the last decade. Facilitated by networking and collaboration tools, there has been a steady concentration of inner-city coworking spaces providing many opportunities for new flexible work arrangements. Driven by sustainability and creative entrepreneurship, coworking spaces are ideal hosting and meeting places to connect creative minds. Despite the growth in inner city coworking spaces, little is known of entrepreneurial needs for coworking models in outer urban city areas, particularly areas that experience rapid population growth. The authors conducted an exploratory study to identify entrepreneurs' coworking needs in a fast-growing outer urban city area in Melbourne, Australia. Drawing on activity theory (AT) as a lens to analyse the data, the study confirms that entrepreneurs in fast-growing outer urban areas have unique coworking needs compared to those in inner-city areas. The study identifies three key requirements that an outer urban coworking model needs to address to support a growing cohort of outer-urban anywhere workers.

Automatic Stereotype Activation Is Context Dependent

2010 ◽  
Vol 41 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Catharina Casper ◽  
Klaus Rothermund ◽  
Dirk Wentura
Keyword(s):  
Lexical Decision ◽  
Context Information ◽  
Decision Task ◽  
Social Categories ◽  
Priming Effects ◽  
Stereotype Activation ◽  
Social Stereotypes ◽  
Priming Paradigm ◽  
Automatic Activation ◽  
Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

Impact of Context Information on Metaphor Elaboration

2018 ◽  
Vol 65 (6) ◽  
pp. 370-384 ◽  
Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss
Keyword(s):  
Diffusion Model ◽  
Processing Speed ◽  
A Priori ◽  
Model Fit ◽  
Verbal Task ◽  
Context Information ◽  
Speed Of Processing ◽  
Good Account ◽  
Dependent Variables ◽  
Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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