Learning Networks Among Swedish Municipalities: Is Sweden a Small World?

Ansell, Christopher; Lundin, Martin; Öberg, PerOla

doi:10.1007/978-3-319-45023-0_15

Cited by 9 publications

(12 citation statements)

References 44 publications

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“…Phase 1 was conducted with larger cities, while the questionnaire survey was distributed to the broader community of local authorities, of which the majority comprised smaller cities. The perception of the majority of these local authorities that geographic proximity was critical when selecting a resilience partner concurs with the findings of Lundin et al (2015) and Ansell et al (2017). This may be because the larger cities have developed a sense of self-sufficiency and tend to be more knowledgeable and well-resourced regarding urban resilience (Mannakkara et al , 2017).…”

Section: Discussionsupporting

confidence: 69%

“…Similarities in such attributes provide a better platform for partners to achieve consensus on solutions applicable to common challenges. The findings on cities preferring resilience partnerships involving similar size and demographic structures echo the results of Lundin et al (2015) and Ansell et al (2017). Regularly updated demographic data and information allow for better informed resilience partner-selection decisions.…”

Section: Discussionmentioning

confidence: 51%

“…Prior relationships were the least ranked criterion in the survey despite discussions in the FGDs of its ability to establish trust. Previous relationships can be viewed from two perspectives: the relationships that exist between the practitioners themselves, and the relationships that may exist between cities as a whole (Ansell et al , 2017; de Villiers, 2009; Lundin et al , 2015). Practitioners’ mutual relationships may be due to prior transactions, work relocations and interactions at conferences and workshops.…”

Section: Discussionmentioning

confidence: 99%

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Criteria for selecting partner cities in a national-level resilience network

Elkhidir

Mannakkara

Wilkinson

2020

IJDRBE

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Purpose The purpose of this paper is to establish the factors affecting the selection of a suitable partner city for resilience building at the national level. Design/methodology/approach An exploratory sequential research was adopted using New Zealand as a case study. Data were collected through focus groups and semi-structured interviews and subsequently validated through an online survey. Findings The study confirmed that the criteria for selecting partner cities for collaboration and knowledge sharing on resilience were similarity of hazards, geographic proximity, city resources and priorities, resilience performance, city size and demographics, previous relationship, willingness to collaborate and similar industries. Practical implications The findings of this paper will help guide cities that are interested in developing national-level resilience partnerships through the process of selecting the most suitable partner cities. Originality/value Despite the existence of international intercity resilience networks, there is a lack of information on the criteria affecting the selection of suitable resilience partner cities at the national level. This paper addresses this gap and offers informed decision-making criteria for cities to consider.

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Section: Discussionsupporting

confidence: 69%

Section: Discussionmentioning

confidence: 51%

Section: Discussionmentioning

confidence: 99%

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Criteria for selecting partner cities in a national-level resilience network

Elkhidir

Mannakkara

Wilkinson

2020

IJDRBE

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“…All 290 managers except one reported that they had learnt something important from at least one other municipality during their last term of office. As we have demonstrated elsewhere, Sweden displays a local learning network where everybody (except one outlier) is connected to one another (Ansell et al forthcoming). Thus, local public managers try to learn from each other's experiences to a great extent.…”

Section: Resultsmentioning

confidence: 92%

Learning From Success: Are Successful Governments Role Models?

Lundin

Öberg

Josefsson

2015

Public Administration

Self Cite

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Do local governments learn from their successful peers when designing public policies? In spite of extensive research on policy diffusion and learning, there is still a lack of studies on how success relates to learning patterns. We address this deficiency by examining which other governments local administrative units draw lessons from. More precisely, we investigate whether public managers learn from the experiences of local governments whose citizens are satisfied with government services. Using a large dyadic dataset on all Swedish municipalities from 2010, we find that senior public managers in local administrations learn from similar local governments and from neighbours. But we also find clear evidence that they learn from local governments whose citizens are pleased with local public services. This indicates that best practices are spread among local governments.

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“…Recent studies have shown that local learning yields results far superior to a global learning strategy, especially on datasets that are not evenly distributed (Ansell et al. 2017 ; Lughofer and Pratama 2017 ; Pratama et al. 2016 ).…”

Section: Introductionmentioning

confidence: 99%

(CF)2 architecture: contextual collaborative filtering

et al. 2018

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Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the everincreasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the (CF) 2 architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering models. The proposed architecture is demonstrated on two large-scale case studies involving over 130 million and over 7 million unique samples, respectively. Results show that contextual models trained with a small fraction of the data provided similar accuracy to collaborative filtering models trained with the complete dataset. Moreover, the impact of taking into account context in real-world datasets has been demonstrated by higher accuracy of context-based models in comparison to random selection models.

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Learning Networks Among Swedish Municipalities: Is Sweden a Small World?

Cited by 9 publications

References 44 publications

Criteria for selecting partner cities in a national-level resilience network

Criteria for selecting partner cities in a national-level resilience network

Learning From Success: Are Successful Governments Role Models?

(CF)2 architecture: contextual collaborative filtering

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