Explaining public risk acceptance of a petrochemical complex: a delicate balance of costs, benefits, and trust

Communities adjacent to polluting industrial facilities understand and evaluate risk in often ambivalent and contextualized ways, not only balancing economic and environmental concerns but also reflecting cultural practices, social worldviews, and trust relationships. In this case study of the Antwerp petrochemical complex, the largest in Europe, a residents’ survey and interviews are used to examine how two middle-class communities coexist with the nearby petrochemical plants. The findings show that citizens in both communities are generally aware of the environmental impact and public health risk but are predominantly accepting of the industry. For both communities, the most important factor explaining acceptance is the perceived socio-economic benefit for the community, while a direct individual benefit in terms of employment does not play a significant role. In one community, risk acceptance is further strengthened by trust in companies’ risk management, while in the other community, trust in regulators is more critical. The different results for both communities stress the importance of a socio-cultural perspective on risk and underline the criticality of relationships of trust. The article further discusses the implications of these findings for environmental decision-making, considering the delicate balance and the significant minority of the population who is less accepting. The present study adds to the risk perception literature by providing one of the first quantitative analyses explaining industrial risk acceptance, instead of perception, using the increasingly contested petrochemical industry as an exemplary case

Distance-Based Image Classification: Generalizing to New Classes at Near Zero Cost

International audienceWe study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new metric learning approach for the latter. We also introduce an extension of the NCM classifier to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over 106 training images of 1,000 classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art, while being orders of magnitude faster. Furthermore, we show how a zero-shot class prior based on the ImageNet hierarchy can improve performance when few training images are available

Image Classification with the Fisher Vector: Theory and Practice

A standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors, encode them into a high dimensional vector and pool them into an image-level signature. The most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements. This leads to the popular Bag-of-Visual words (BOV) representation. In this work, we propose to use the Fisher Kernel framework as an alternative patch encoding strategy: we describe patches by their deviation from an ''universal'' generative Gaussian mixture model. This representation, which we call Fisher Vector (FV) has many advantages: it is efficient to compute, it leads to excellent results even with efficient linear classifiers, and it can be compressed with a minimal loss of accuracy using product quantization. We report experimental results on five standard datasets -- PASCAL VOC 2007, Caltech 256, SUN 397, ILSVRC 2010 and ImageNet10K -- with up to 9M images and 10K classes, showing that the FV framework is a state-of-the-art patch encoding technique

Arbeidsimmigratie naar Nederland

Deze studie gaat over de immigratie van vreemdelingen die komen werken. Wij hanteren de begrippen arbeidsimmigrant en buitenlandse werknemer door elkaar. Deze studie gaat in eerste instantie niet over vreemdelingen die voor een ander doel komen zoals het vragen van asiel of voor gezinshereniging, en hun positie op de arbeidsmarkt. Ook wordt in de door ons geraadpleegde bronnen onderscheid gemaakt tussen tijdelijke arbeidsimmigratie en permanente arbeidsimmigratie. Het begrip tijdelijke arbeidsimmigratie kan betekenen dat een arbeidsimmigrant volgens de regels maar een maximaal aantal maanden of jaren mag blijven, zoals seizoensarbeiders of een universitair gastdocent (juridisch tijdelijk). Uit cijfers van het CBS blijkt dat een groot aantal arbeidsimmigranten binnen zes jaar uit Nederland vertrekt (feitelijk tijdelijk). Van permanente arbeidsimmigratie is sprake als het de arbeidsimmigrant is toegestaan zich permanent te vestigen (juridisch permanent). Of de arbeidsimmigrant dat ook daadwerkelijk doet is daarmee nog niet gezegd. Er zijn ook arbeidsimmigranten die in eerste instantie tijdelijk komen maar toch langer blijven, bij dezelfde of een andere werkgever, al dan niet illegaal, of op grond van bijvoorbeeld gezinsvorming (feitelijk permanent)

The ‘just’ management of urban air pollution? A geospatial analysis of low emission zones in Brussels and London

The increasing evidence base and public concern on the health effects of exposure to high levels of air pollution, combined with stricter environmental legislation, are forcing local governments to take drastic measures. One of the policy instruments, the low emission zone (LEZ), specifically targets a reduction in emissions from vehicles, a key source in urban environments. It is a contested instrument, with supporters who think it is a fair “polluter pays” instrument that especially benefits more deprived communities, while opponents fear an unequal social impact on people's accessibility and finances. This study wants to add a data-driven perspective to the discussion by simultaneously analysing the unequal exposure to air pollution and the unequal accessibility impact, in a comparative study of the LEZs in London and Brussels. The analysis combines a conventional multivariate regression analysis with a geographically weighted regression (GWR) modelling to define the local spatial variation in the relationships, which is of particular concern when considering an explicitly spatial problem and solution. The study shows that GWR is a promising method in distributional environmental justice research through identifying parts of the city where effects are more unequal, as such facilitating customized policy instruments and targeted support

Ambient Intelligence and Persuasive Technology: The Blurring Boundaries Between Human and Technology

The currently developing fields of Ambient Intelligence and Persuasive Technology bring about a convergence of information technology and cognitive science. Smart environments that are able to respond intelligently to what we do and that even aim to influence our behaviour challenge the basic frameworks we commonly use for understanding the relations and role divisions between human beings and technological artifacts. After discussing the promises and threats of these technologies, this article develops alternative conceptions of agency, freedom, and responsibility that make it possible to better understand and assess the social roles of Ambient Intelligence and Persuasive Technology. The central claim of the article is that these new technologies urge us to blur the boundaries between humans and technologies also at the level of our conceptual and moral frameworks

Simultaneous visualization of language endangerment and language description

The world harbors a diversity of some 6,500 mutually unintelligible languages.As has been increasingly observed by linguists, many minority languages are be-coming endangered and will be lost forever if not documented. Urgently indeed,many efforts are being launched to document and describe languages. This under-taking naturally has the priority toward the most endangered and least describedlanguages. For the first time, we combine world-wide databases on language de-scription (Glottolog) and language endangerment (ElCat, Ethnologue, UNESCO)and provide two online interfaces, GlottoScope and GlottoVis, to visualize thesetogether. The interfaces are capable of browsing, filtering, zooming, basic statis-tics, and different ways of combining the two measures on a world map back-ground. GlottoVis provides advanced techniques for combining cluttered dotson a map. With the tools and databases described we seek to increase the overallknowledge of the actual state language endangerment and description worldwid

Determinants of intraregional migration in Sub-Saharan Africa 1980-2000

Despite great accomplishments in the migration literature, the determinants of South-South migration remain poorly understood. In an attempt to fill this gap, this paper formulates and tests an empirical model for intraregional migration in sub-Saharan Africa within an extended human capital framework, taking into account spatial interaction. Using bilateral panel data between 1980 and 2000, we find that intraregional migration on the subcontinent is predominantly driven by economic opportunities and sociopolitics in the host country, facilitated by geographical proximity. The role played by network effects and environmental conditions is also apparent. Finally, origin and destination spatial dependence should definitely not be ignored