As multifunctional places that combine shopping and hospitality with public space and residential functions, urban consumption spaces are sites where different normative orders surface and sometimes clash. In Amsterdam, such a clash emerged over touristification of consumption spaces, eroding place attachment for local residents and urging the city government to take action. Based on policy analysis and interviews with entrepreneurs and key informants, we demonstrate how Amsterdam’s city government is responding to this issue, using legal pluralism that exists within formal state law. Specifically, the city government combines four instruments to manage touristification of consumption spaces, targeting so-called tourist shops with the aim to drive them out of the inner city. This strategic combination of policy instruments designed on various scales and for different publics to pursue a local political goal jeopardizes entrepreneurs’ rights to legal certainty. Moreover, implicitly based on class-based tastes and distrust towards particular minority groups of entrepreneurs, this policy strategy results in institutional discrimination that has far-reaching consequences for entrepreneurs in itself, but also affects trust relations among local stakeholders.
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Multilevel models using logistic regression (MLogRM) and random forest models (RFM) are increasingly deployed in industry for the purpose of binary classification. The European Commission’s proposed Artificial Intelligence Act (AIA) necessitates, under certain conditions, that application of such models is fair, transparent, and ethical, which consequently implies technical assessment of these models. This paper proposes and demonstrates an audit framework for technical assessment of RFMs and MLogRMs by focussing on model-, discrimination-, and transparency & explainability-related aspects. To measure these aspects 20 KPIs are proposed, which are paired to a traffic light risk assessment method. An open-source dataset is used to train a RFM and a MLogRM model and these KPIs are computed and compared with the traffic lights. The performance of popular explainability methods such as kernel- and tree-SHAP are assessed. The framework is expected to assist regulatory bodies in performing conformity assessments of binary classifiers and also benefits providers and users deploying such AI-systems to comply with the AIA.
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The six-minute walking test (6MWT) may be a practical test for the evaluation functional exercise capacity in children with end-stage renal disease (ESRD). The aim of this study was to investigate the 6MWT performance in children with ESRD compared to reference values obtained in healthy children and, secondly, to study the relationship between 6MWT performance with anthropometric variables, clinical parameters, aerobic capacity and muscle strength. Twenty patients (13 boys and seven girls; mean age 14.1 ± 3.4 years) on dialysis participated in this study. Anthropometrics were taken in a standardized manner. The 6MWT was performed in a 20-m-long track in a straight hallway. Aerobic fitness was measured using a cycle ergometer test to determine peak oxygen uptake (V⋅O2peak)(V⋅O2peak), peak rate (Wpeak) and ventilatory threshold (VT). Muscle strength was measured using hand-held myometry. Children with ESRD showed a reduced 6MWT performance (83% of predicted, p < 0.0001), irrespective of the reference values used. The strongest predictors of 6MWT performance were haematocrit and height. Regression models explained 59% (haematocrit and height) to 60% (haematocrit) of the variance in 6MWT performance. 6MWT performance was not associated with V⋅O2peakV⋅O2peak, strength, or other anthropometric variables, but it was significantly associated with haematocrit and height. Children with ESRD scored lower on the 6MWT than healthy children. Based on these results, the 6MWT may be a useful instrument for monitoring clinical status in children with ESRD, however it cannot substitute for other fitness tests, such as a progressive exercise test to measure V⋅O2peakV⋅O2peak or muscle strength tests.
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Multilevel models (MLMs) are increasingly deployed in industry across different functions. Applications usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects: model, discrimination, and transparency & explainability. These aspects are subsequently divided into sub-aspects. Contributors, such as inter MLM-group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub-aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs, among others, intergroup individual fairness (DiffInd_MLM) across MLM-groups, probability unexplained (PUX) and percentage of incorrect feature signs (POIFS). A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency & explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling.Using an open-source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution on the outcome). For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit providers, users, and assessment bodies, as defined in the European Commission’s proposed Regulation on Artificial Intelligence, when deploying AI-systems such as MLMs, to be future-proof and aligned with the regulation.
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The Kenyan supreme court recently struck down a government decision to ban the registration of an LGBTIQ+ community rights organisation, sparking new homophobic rhetoric in the country. Kenya is one of 32 African countries that criminalises homosexuality. Those who identify as part of the LGBTIQ+ community are often discriminated against, harassed and assaulted. Lise Woensdregt and Naomi van Stapele, who have researched queer experiences in Kenya for nine years, explain the impact of this ruling.
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Background: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration. Results: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56-0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63-0.73; PHL was 0.658). Discussion: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.
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During an interview at Georgetown University’s School of Foreign Service one student questioned Prime Minister Rutte about an official apology for slavery. The Dutch Prime Minister assured that each island-nation to whom the Kingdom apologized “has full power to decide to leave the Kingdom. They are not colonized. They are independent.” Rutte described the current role of The Netherlands as that of a “gateway” to bring their products to Europe. The emphasis on trade relationship smacks of neo-colonial interests. Rutte’s portrayal of The Netherlands acting as the “in” to the European market for the former colonies is far from the recovery that one would expect for the descendants of the enslaved. In fact, the Slavery Past Dialogue made a number of recommendations to the Dutch Kingdom, including “active prevention of discrimination and institutional racism throughout society” and “the establishment of a Kingdom Fund […] for structural and sustainable financing of recovery measures.” The Dutch Prime Minister’s comments belie a singular focus on trade with the Caribbean nations rather than a holistic approach, looking at non-pecuniary interests involving the well-being of the descendants and the societies in which they live today. The “republicanization” serves as a backdrop to the years-long journey during which the Dutch government (and the Dutch crown) seemingly dragged their feet, refusing to issue a formal apology for the trade of Africans by the Dutch West Indies corporation. That much-solicited apology was finally issued in December 2022, despite warnings that any gesture that excluded reparations would not be favorably received by the Dutch Caribbean nations.
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Spottingtechnologieën en -technieken worden steeds vaker ingezet om afwijkend gedrag te ontdekken. Dit artikel laat zien dat hierbij verschillende ethische vragen spelen. Zo kan er door het gebruik van technologieën ‘discrimination by design’ ontstaan. Bovendien heeft afwijkend gedrag volgens de sociologie een rol te spelen in onze maatschappij. Export van spottingtechnologieën naar landen met een meer restrictief regime kan soms leiden tot het verergeren van mensenrechtenschendingen. Een vraag is ook wat een passende reactie is op het ontdekken van afwijkend gedrag. Tot slot, leidt eventuele registratie van afwijkend gedrag tot verschillende problemen.
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Titel: The Exodus from the Netherlands or Brain Circulation: Push and Pull Factors of Remigration among Highly Educated Turkish Dutch Auteurs: Gürkan Çelik and Ton Notten In: European Review, 22 (3), 2014, pp. 403-413 The exodus from the Netherlands or brain circulation: Push and pull factors of remigration among highly educated Turkish-Dutch An increasing number of Turks, the Netherlands’ largest ethnic minority, are beginning to return to their country of origin, taking with them the education and skills they have acquired abroad, as the Netherlands faces challenges from economic difficulties, social tension and increasingly powerful right wing parties. At the same time Turkey’s political, social and economic conditions have been improving, making returning home even more appealing for Turkish migrants at large. This article gives explanations about the push and pull factors of return migration. The factors influencing return to one’s country of origin are “pulls”. It is assumed that remigration is more affected by positive developments in the country of origin than by negative developments in the country of residence. Civil society, business world and the Dutch government can develop policies to bind these capable people to the Netherlands, at least in the form of “brain circulation” so that they can serve as “bridge builders” between the two countries. Keywords Return migration, integration, Turkish-Dutch, Turkish migrants, brain circulation =============================================================================== SAMENVATTING De uittocht uit Nederland of breincirculatie: Push- en pull-factoren van remigratie onder hoogopgeleide Turkse Nederlanders. In Nederland zien we een lichte toename van het aantal Turken, de grootste etnische minderheidsgroep in Nederland, die terugkeren naar hun land van herkomst. Ze exporteren daarmee goede opleidingen en vaardigheden die ze in Nederland verwierven. De oorzaken: de economische neergang, sociale spanningen en de groeiende invloed van extreemrechtse partijen. Tegelijkertijd verbeteren in Turkije de politieke, sociale en economische omstandigheden die steeds meer aantrekkingskracht uitoefenen op immigranten in dat land. Dit artikel gaat in op de push- and pull-factoren voor remigranten. Pull-factoren beinvloeden iemands terugkeer naar zijn land van herkomst. Aangenomen wordt dat zo’n remigratie sterker wordt bevorderd door positieve ontwikkelingen in het land van herkomst dan door negatieve (push-factoren) in het land waar men op dat moment woont. De civil society, het bedrijfsleven en de Nederlandse overhead kunnen een beleid ontwikkelen dat verdienstelijke inwoners weet te behouden, hen op z’n minst kan inschakelen als bruggenbouwers en aldus kenniscirculatie mogelijk maakt tussen beide landen. Trefwoorden Retourmigratie, integratie, Turkse Nederlanders, Turkse migranten, kenniscirculatie, breincirculatie
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Objective: To describe the discrimination and calibration of clinical prediction models, identify characteristics that contribute to better predictions and investigate predictors that are associated with unplanned hospital readmissions.Design: Systematic review and meta-analysis.Data source: Medline, EMBASE, ICTPR (for study protocols) and Web of Science (for conference proceedings) were searched up to 25 August 2020.Eligibility criteria for selecting studies: Studies were eligible if they reported on (1) hospitalised adult patients with acute heart disease; (2) a clinical presentation of prediction models with c-statistic; (3) unplanned hospital readmission within 6 months. Primary and secondary outcome measures: Model discrimination for unplanned hospital readmission within 6 months measured using concordance (c) statistics and model calibration. Meta-regression and subgroup analyses were performed to investigate predefined sources of heterogeneity. Outcome measures from models reported in multiple independent cohorts and similarly defined risk predictors were pooled.Results: Sixty studies describing 81 models were included: 43 models were newly developed, and 38 were externally validated. Included populations were mainly patients with heart failure (HF) (n=29). The average age ranged between 56.5 and 84 years. The incidence of readmission ranged from 3% to 43%. Risk of bias (RoB) was high in almost all studies. The c-statistic was <0.7 in 72 models, between 0.7 and 0.8 in 16 models and >0.8 in 5 models. The study population, data source and number of predictors were significant moderators for the discrimination. Calibration was reported for 27 models. Only the GRACE (Global Registration of Acute Coronary Events) score had adequate discrimination in independent cohorts (0.78, 95% CI 0.63 to 0.86). Eighteen predictors were pooled. Conclusion: Some promising models require updating and validation before use in clinical practice. The lack of independent validation studies, high RoB and low consistency in measured predictors limit their applicability.PROSPERO registration number: CRD42020159839.
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