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Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.

Poverty is a multidimensional social phenomenon that occurs in every economy around the world. Counteracting poverty is one of the tasks of public administration units. Under many programs financed from public funds, various tools and instruments can be used to combat poverty, but their implementation should be preceded by an in-depth analysis of the effects generated by their use. This is not easy, because the phenomenon of poverty is very complex, results from the arrangement of many interrelated heterogeneous elements, and the effects of actions are visible only after a long time. Hence, research in this area requires the use of an approach that can cope with the complexity of this phenomenon in dynamic terms. The aim of the article is to present the concept of a hybrid simulation model for studying the impact of public intervention on the level of poverty at local, regional and national level. The model is a hybrid of two computer simulation methods: System Dynamics (SD) and Agent-Based Simulation (ABS). SD method is used to model macroelements of the examined system (e.g. GDP level, labor market) and microelements (e.g. households and their members) are modeled using ABS. The article also shows the results of verification and validation of the proposed solution performed using the model for a case study. The presented solution can be used both by public administration units at various levels as well as by scientists - to conduct socio-economic research.

Big Data is an emerging field in organizational research as it provides new types of data, and technologies like digitization and web scraping allow to study huge amounts of data. Since large parts of digital data consist of unstructured text, text classification - assigning texts (or parts of texts) to predefined categories - is a central task. Text classification not only allows to identify relevant texts in a jumble of data but also to extract information from texts, such as sentiments, topics, and intentions. However, large amounts of textual data require the use of automated text mining methods, which is mostly uncharted territory in organizational research. We, therefore, outline and discuss the two existing approaches to text classification, one originating from social science (dictionary content analysis) the other from computer science (supervised machine learning). Since both approaches have advantages and disadvantages, we combine ideas from both to develop a hybrid approach that reduces existing issues and requires significantly less knowledge in programming and computer science than supervised machine learning. To illustrate our approach, we develop a classifier that identifies critical media coverage of organizational actions.

The emergence of social media and digital technologies has resulted in new protectionist laws, policies, and mandates aimed at regulating the sexual behaviour of women and girls in online spaces. These neoliberal responsiblization strategies are aimed at shaping good, young digital citizens and have become further amplified through increased concerns about domestic human trafficking and victim vulnerability. This protectionism, however, is not always reflected in courtroom proceedings, revealing a tension between the protection and responsiblization of victims of trafficking in Canada. Using R v Oliver-Machado (2013) as a case study, we examine the ways in which the defence counsel's reliance on commonplace defence tactics used in sexual assault cases responsibilize the young complainants in an attempt to discredit their victimhood and reconstruct them as online sexual risk takers.

This paper explores the uses of YouTube by Uyghur nationalist movement activists and studies various ideological codes used by different communities to promote their messages. It argues that several ideological codes are produced in order to challenge the dominant ideologies promoted by the Chinese government, which create a ground for Uyghur 'imagined solidarity' across physical borders. Analysis of the production of audio-visual messages by the dispersed ethnic group provides an important window into how ethnic identity is forged by means of Web 2.0. (J Contemp China/GIGA)

N° ISBN - 978-2-7380-1284-5 ; International audience ; Over the past decade, the Dutch Government has increasingly emphasised the need for integral solutions for sustainability problems in the livestock production sector. This led to the adoption of research approaches in line with transition management and system innovation that had been developed in other domains. In 2008, the government set further policy targets of 5% and 100% sustainable livestock production at the farm level for 2011 and 2023 respectively. Policy measures included stimulation of sector initiatives for sustainable agriculture (sectoral innovation agendas) and demand for projects with a focus on system innovation. Two broad approaches may contribute to the realization of these targets, notably top down and bottom up. Top down approaches are usually research-led and characterized by the formulation of visions of future livestock production systems. At the same time a broad variety of bottom up initiatives is taken by farmers who develop and try out new approaches to meet the challenges as they see them. Currently, the links between the bottom up and the top down processes are relatively weak. As both may contribute to a system innovation, a major challenge is to make a fruitful combination between the two approaches. To this end we have developed what we call a "Learning and Experimentation Strategy" (LES) that we will elaborate in the paper.

N° ISBN - 978-2-7380-1284-5 ; International audience ; Over the past decade, the Dutch Government has increasingly emphasised the need for integral solutions for sustainability problems in the livestock production sector. This led to the adoption of research approaches in line with transition management and system innovation that had been developed in other domains. In 2008, the government set further policy targets of 5% and 100% sustainable livestock production at the farm level for 2011 and 2023 respectively. Policy measures included stimulation of sector initiatives for sustainable agriculture (sectoral innovation agendas) and demand for projects with a focus on system innovation. Two broad approaches may contribute to the realization of these targets, notably top down and bottom up. Top down approaches are usually research-led and characterized by the formulation of visions of future livestock production systems. At the same time a broad variety of bottom up initiatives is taken by farmers who develop and try out new approaches to meet the challenges as they see them. Currently, the links between the bottom up and the top down processes are relatively weak. As both may contribute to a system innovation, a major challenge is to make a fruitful combination between the two approaches. To this end we have developed what we call a "Learning and Experimentation Strategy" (LES) that we will elaborate in the paper.