“Who generates this city”? Socialist strategy in contemporary London

This essay, based on a “militant ethnography” of the attempts of the small radical grassroots activist group, Our London (a pseudonym), to mobilize a collective oppositional politics through activities around an election campaign, engages critically with E. Laclau and C. Mouffe's arguments on discourse and collectivity in Hegemony and Socialist Strategy (London: Verso, 1985). I argue, on the basis of my findings, that while their model does provide insights that help describe the process of building collectivity from among disparate perspectives and identities, we need to go beyond a focus on discourse alone and consider the ways politics is shaped by material contexts. This is necessary if we are to understand the continued appeal of class politics as well as the difficulties in mobilizing collectivity in highly unequal and fragmented cities. From an activist perspective, the essay also highlights how developing a conception of collective interests and a critique of overarching systems of exploitation can be important in building political unity.     

Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

A Bayesian Model and Stochastic Exposure (Dose) Estimation for Relative Exposure Risk Comparison Involving Asbestos‐Containing Dropped Ceiling Panel Installation and Maintenance Tasks

Assessing exposures to hazards in order to characterize risk is at the core of occupational hygiene. Our study examined dropped ceiling systems commonly used in schools and commercial buildings and lay‐in ceiling panels that may have contained asbestos prior to the mid to late 1970s. However, most ceiling panels and tiles do not contain asbestos. Since asbestos risk relates to dose, we estimated the distribution of eight‐hour TWA concentrations and one‐year exposures (a one‐year dose equivalent) to asbestos fibers (asbestos f/cc‐years) for five groups of workers who may encounter dropped ceilings: specialists, generalists, maintenance workers, nonprofessional do‐it‐yourself (DIY) persons, and other tradespersons who are bystanders to ceiling work. Concentration data (asbestos f/cc) were obtained through two exposure assessment studies in the field and one chamber study. Bayesian and stochastic models were applied to estimate distributions of eight‐hour TWAs and annual exposures (dose). The eight‐hour TWAs for all work categories were below current and historic occupational exposure limits (OELs). Exposures to asbestos fibers from dropped ceiling work would be categorized as “highly controlled” for maintenance workers and “well controlled” for remaining work categories, according to the American Industrial Hygiene Association exposure control rating system. Annual exposures (dose) were found to be greatest for specialists, followed by maintenance workers, generalists, bystanders, and DIY. On a comparative basis, modeled dose and thus risk from dropped ceilings for all work categories were orders of magnitude lower than published exposures for other sources of banned friable asbestos‐containing building material commonly encountered in construction trades.     

Danish and Norwegian hospital social workers’ cross-institutional work amidst inter-sectoral restructuring of health and social welfare

Starting in the 2000s, Denmark and Norway have undergone extensive restructuring of their health-related social benefit programmes, including how they are governed. Several reforms have sought to enhance inter-sectoral collaboration. Aiming at ensuring patients’ faster return to work, policy-makers have instituted economic incentives to both individuals and the health and welfare organisations who handle them. Through an institutional logics approach, this paper explores how hospital social workers in these countries are experiencing these changes. The ‘social’ part of post-treatment care and rehabilitation receives more attention in the Norwegian institutional set-up than in the Danish, and whilst challenges are experienced in both countries, in group interviews Danish social workers in particular express concerns about the implications of the accelerated return-to-work focus. In both countries, they report increasing difficulties in ‘making their way through’ the state-municipal bureaucracy. However, by drawing on the formal health knowledge derived from medical settings and the symbolic capital it bestows on them, they often manage to negotiate the work-and-welfare services, thereby transforming the social context for the patients.     

Policy instrument affordances: a framework for analysis

The present study uses the concept of technological efficacy derived from ecological psychology and design studies to offer an alternative way of analyzing how policy instruments affect change. Reasoning from this, the paper outlines a framework for analyzing policy instruments in terms of their affordances. We define affordances as the means through which an instrument exerts influence on its intended target audience. Using this approach, we contend that policy instruments may be analyzed as interfaces that organize social relations and create structures of opportunity and/or restrict possibilities for action. We argue that explicating the pathways through which instruments afford or constrain action is a central task for policy analysis. Our proposed framework of analysis builds on the idea that instruments yield effects by facilitating action and learning. We further contend that the actions that an instrument can facilitate or inhibit are determined by specific affordance modalities of the instrument in conjunction with contingencies of the actor and the policy environment. Examples from research policy are used to illustrate some of these effects.     

Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models

Outlier detection algorithms are intimately connected with robust statistics that down‐weight some observations to zero. We define a number of outlier detection algorithms related to the Huber‐skip and least trimmed squares estimators, including the one‐step Huber‐skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.