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Mega-events have long been promoted as contributing to long-term development strategies, generating much interest in the legacy of these events (Basso, 2017; Gold and Gold, 2017; Morandi and Di Vita, [...]

Recent debates regarding urban policy mobilities have shed light on the transfer and adaptation of similar sets of measures and projects. However, limited attention has been given to the local decision-making processes and to how democratic or autocratic modes of governance affect the adaptation. This paper focuses on the transnational spreading of brand museums and cultural institutions as part of leisure-and-tourism-oriented urban regeneration strategies. The Guggenheim Foundation has been most active in this regard since the late 1990s with the Bilbao narrative de-contextualised and circulating internationally, luring new localities to establish the museum. The paper compares two fairly similar Guggenheim museum initiatives in situations differing radically in political and planning terms in Abu Dhabi, UAE and Helsinki, Finland – respectively, the capital of a sheikdom and the capital of a democratic country – asking what difference the mode of governance has made in the re-contextualisation of the projects. The comparison shows that the democratic governance generated a high degree of collective intelligence, involved multiple stakeholders and allowed critical evaluations beneficial in decreasing the risk-proneness, emphasising the site-specificity and bargaining for the city’s benefit. The authors call for a more nuanced understanding of the local governance within the re-contextualisation process of such initiatives and for further research regarding the transnational strategies of ‘auxiliary institutions’.

We argue that political sciexntists can provide additional evidence for the predictive validity of observational and quasi-experimental research designs by minimizing the expected prediction error or generalization error of their empirical models. For observational and quasi-experimental data not generated by a stochastic mechanism under the researcher’s control, the reproduction of statistical analyses is possible but replication of the data-generating procedures is not. Estimating the generalization error of a model for this type of data and then adjusting the model to minimize this estimate—regularization—provides evidence for the predictive validity of the study by decreasing the risk of overfitting. Estimating generalization error also allows for model comparisons that highlight underfitting: when a model generalizes poorly due to missing systematic features of the data-generating process. Thus, minimizing generalization error provides a principled method for modeling relationships between variables that are measured but whose relationships with the outcome(s) are left unspecified by a deductively valid theory. Overall, the minimization of generalization error is important because it quantifies the expected reliability of predictions in a way that is similar to external validity, consequently increasing the validity of the study’s conclusions.