Street Level Research Articles

Street-level bureaucrats’ attitudes towards clients in discretionary decision-making: Evidence from the Norwegian labour and welfare administration

It is commonly assumed that the attitudes of street level bureaucrats (SLBs) towards benefit recipients influence the discretionary decisions they make. Although discretion in the policy process has been a contentious issue, the impact of bureaucrats’ attitudes has received limited scholarly attention. This study investigates the impact of attitudes on discretionary decision-making and whether the impact of attitudes is conditioned by the SLBs’ perception of recipients’ conduct, when sanctioning non-compliance of requirements among social assistance recipients. The study employs survey data from 925 bureaucrats within the Norwegian Labour and Welfare Administration, measuring four attitude components: cognitive, positive affective, negative affective, and behavioral attitudes. A vignette experiment allows assessing how sensitive discretionary decision-making is to SLBs’ perceptions of recipient conduct. The findings indicate that cognitive attitudes significantly impact discretionary decision-making and are sensitive to SLBs’ perceptions of the recipients’ personal responsibility. Prejudiced cognitive attitudes (viewing recipients as dishonest, manipulative, unpredictable, or stubborn) are associated with a greater tendency to attribute non-compliance to a lack of willingness, irrespective of external circumstances. The study contributes to a deeper understanding of discretionary decision-making and sheds light on the complex dynamics between SLBs’ attitudes and their perceptions of client attributes, offering new insight for public policy and administration.

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

Resilient health care performance in the real world: fixing problems that never happened

BackgroundStaff in health systems everywhere have exhibited flexibility and a capacity for improvisations during, and in response to, the COVID-19 pandemic. Looking to other examples of such resilient behaviours outside of those induced by the pandemic is instructive for those involved with researching or understanding change, or making health systems improvements.MethodsHere, we synthesise and then assess the value of eight case studies of in situ resilient performance from Canada, Sweden, Japan, Belgium, the United Kingdom, Norway, the United States and Brazil. The cases are divided into four categories: responsiveness to a crisis; adaptiveness over time; local adoption in accommodating to a top down, national policy change; and the consequential outcomes of an intervention.ResultsThe cases illuminate the resourcefulness of translational and social researchers in examining such behaviours and practices. More than that, they also foreground the ingenuity and adaptive capacity of staff on-the-ground who continually anticipate, respond and adapt to make systems work and provide continuous care in the face of many challenges, including resource deficiencies, policy misalignments, and new technologies, policies and procedures that need to be integrated into local workflows. Front line clinicians make care systems work, pre-empting issues and sorting out problems before they occur or as they arise.ConclusionsA key lesson amongst a range of findings is that, rather than focusing on shiny new tools of change (checklists, frameworks, policy mandates), it is much more insightful and satisfying to deeply apprehend care at the sharp end, where clinicians deliver care to patients, understanding how everyday work is executed. This, rather than the Health Ministry, the Boardroom, or the Management Consultant’s office, is where and how change is being enabled, and where street level actors solve problems, thwart issues in advance, and constantly avoid pitfalls.

The struggle for linking strategic and front-line outcomes in local government service delivery

ABSTRACT Over the last 50 years, there has been a transition in articulating local government service delivery, from replacing output-based terminology to an outcomes-based approach. This is understandable given the move from traditional public administration to an increasingly marketised landscape. However, there is a gap between the higher-level strategic outcome lens compared with outcomes pursued by local government street level bureaucrats focusing on casework. Whereas strategic outcomes are positioned at population, target focused or place-based data, at the frontline outcomes are built on personal relationships which emphasise emotional factors. In this paper, we consider the growth of an outcomes-based approach in local government. We also discuss the extent to which the strategic and street level outcome levels can be linked, examining some of the potential mechanisms available to do so and considering whether the ongoing tensions can ever be resolved. We conclude by considering implications for academia in addressing these challenges.

Exploring the Impact of Urban Amenities on Business Circle Vitality Using Multi-Source Big Data

Urban business circles are important locations for economic and social activities. Improving the vitality of urban business circles is conducive to stimulating the potential of the consumer market and promoting sustainable economic development. However, targeted research on the factors influencing business circle vitality is lacking. Therefore, in this study, we aimed to quantitatively examine the impact of the number and diversity of urban amenities on business circle vitality at the street block level using open-source geospatial big data from 32 Chinese metropolises. We found that the number of residential, transportation, educational, cultural, and recreational amenities and the diversity of catering and retail amenities had significant positive impacts on business circle vitality. Catering and retail diversity were the two most critical factors, followed by the number of transportation, cultural, and recreational amenities. However, the effect of urban amenities on business circle vitality varied considerably across different cities and business districts. The results of this study contribute to a holistic understanding of how to improve the vitality of business circles by optimizing urban amenities at the street block level.

Scale effect on the relationship between urban landscape patterns and land surface temperature

Great efforts have been made to examine the linkages between land surface temperature (LST) and urban landscape patterns (ULPs), which, however, focus on a single spatial scale and linear relationships. This study aims to examine the influence range of four key ULP indicators on LST, namely sky view factor (SVF) and three indices of built-up area, greens, and blue spaces. The analyses are conducted at street block level in 38 big cities in China, and leverage a multiscalar approach to investigate change patterns of indicators within multiple buffer zones and identify the buffer distance that yields the greatest influence. Results reveal that (1) the greatest local impacts are produced within 0−150 m buffer zones in most cities for all key ULP metrics; (2) the maximum impacts of most indicators in summer are greater than other seasons; and (3) the influence magnitude of key ULP indicators increase after considering scale effect, and the influence of SVF varies significantly across cities. Results suggest that the consideration of maximum influence ranges of ULP indicators can better explain LST spatial variations. Our findings offer evidence on the local impact of ULPs on LST and contribute to urban design in urban heat mitigation.

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

Optimal representation of tree foliage for local urban climate modeling

Trees impact the local urban climate, notably at street level by intercepting solar radiation and providing shading. Evapotranspiration in foliage may reduce the air temperature although it may increase relative humidity and leaf drag may reduce wind speed, affecting thermal comfort. To document and quantify this impact, microclimate modeling with Computational Fluid Dynamics (CFD) simulations requires explicit information of the urban configuration, including trees. However, trees are complex individuals with a variety of shapes and a variety of foliage distribution. This study aims to investigate the sensibility to the tree modeling of the urban climate simulations. Starting with terrestrial LiDAR data from trees of different species, ages, and forms, we propose a systematic evaluation of the optimal representation of arboreal configurations in terms of local urban comfort. One way to represent the foliage of trees accurately is to apply Delaunay triangulation on the LiDAR data, which yields a convex envelope model. The resulting foliage shape is very close to the actual tree, but includes a high number of facets leading to complex objects to model numerically. Comparing four species and three maturity level of trees with this method, the paper shows that the size of the zone shadowed by a tree is the parameter with the largest impact on thermal comfort, as the ability of trees to absorb solar radiation is the main asset to improve thermal comfort. The UTCI could be up to 2.1°C lower for a mature ACPL than for a sapling, mainly because the zone covered by the tree is larger. In addition, polyhedron shape rhombicuboctahedron (RBC) produces accurate shadowed zones. Mostly, in literature, tree canopies are modeled with cubic representations while we see that they overestimate the size of the shadowed zone. To have reliable compromise between accuracy and time for conception and computational time, this paper shows that the RBC is the best alternative to common tree models. Despite requiring a good knowledge of the canopy geometry, RBC provides a strong capacity for accurately modelling complex canopy shapes of most tree species and offers large benefits in reduced complexity. We show that the RBC shape, thanks to its simple but flexible geometry, is an efficient and accurate methodological approach to model trees and allows savings in computational time (up to 15% faster than the convex envelope) and costs; and we expect that this method will improve the modeling of further parametric studies on vegetation impact on thermal urban comfort.

Enhancing outdoor comfort in urban hot climates: investigating the cooling impact of two tropical trees through shade provision and transpiration

ABSTRACT One key ecosystem service widely known and attributed to trees in the urban environment is their ability to provide shade through their canopies and undertake evapotranspiration cooling. These in turn contribute to reduced atmospheric temperatures. A lesser-known aspect with little quantitative data is the heterogeneous temperature environment beneath the canopies at pedestrian or street level where the cooling will benefit thermal comfort. This study was conducted throughout 2022. It collected data where air temperature was measured under the canopies of two urban tropical tree species, Samanea saman and Peltophotum pterocarpum, at three different heights, which were 1, 3 and 5 m, respectively, from the ground. This was to investigate whether crown architectures and the associated shade and cooling effects via transpiration can have a significant change in the surrounding temperature. The study observed surface and air temperatures coupled with meteorological data across cool, warm, and hot periods of the year. It was observed that soil temperature increased alongside increases in atmospheric temperatures with a more significant rise observed for P. pterocarpum despite the observation of greater wind speeds for both night and day at the site where this species was situated. The soil moisture potential data indicated the reverse where S. saman showed more negative values with the greatest increase under warm conditions. Interestingly, sap flow rates also indicated that P. pterocarpum had more efficient rates whether conditions were cool, warm, or hot, suggestive of more active transpirational cooling. This was despite a lower leaf area index, smaller crown volume and sap wood area for this species. Relationship analyses between sap flow and air temperatures at different heights under the canopies showed a positive correlation. This demonstrates that apart from shade provided by the canopies, transpiration is also an important parameter for cooling. The mean daytime air temperature indicated that the greatest cooling effect was experienced at the lowest level from the ground (at 1 m), and this was consistent for both species across cool, warm, and hot conditions with a significant cooling effect of between 2.3 and 4°C. Temperature data under the canopy (1 to 5 m from the ground) compared to the air temperature of the grass that was shaded indicated temperature differences of between 2.3 and 7.9°C at 5 m and between 1.9 and 5.6°C at 1 m (for both species) with a bigger reduction attributed to S. saman.

GPs' experience of difficult decisions in patients with dementia and an acute illness.

GPs are often required to make decisions about the management of acute illness in people living with dementia. These decisions are often complex and involve multiple informants. We aimed to explore how GPs made decisions about acute illness in people with dementia using a micropolitics approach. Qualitative, semi-structured interviews with 13 GPs with a range of years of experience working in South Yorkshire, UK. Interviews were conducted by phone. Interviews focused on GPs' accounts to reflect their own perceptions and choices as portrayed to the interviewer. The analysis used the lens of micro-politics in the analysis and interpretation of the themes, with a focus on decisions between GP, patient, family and carers, other health and social care providers about the management of acute illness in a person with dementia. The results showed that GPs act as street-level bureaucrats while carrying out their role, using discretion during decision-making in an environment characterised by uncertainties and work pressures. In addition, GPs use the "soft power" skills of diplomacy such as negotiation, persuasion and engagement in navigating through difficult decision-making situations, while building relationships and partnerships with various actors in the health system. GPs possess and express power and influence decision-making in people living with dementia when navigating biomedical, social, economic and psychological factors. This power comes in the form of soft power (street level diplomacy) and the more formal power of street level bureaucracy.

SAGE-GSAN: A graph-based method for estimating urban taxi CO emissions using street view images

Accurately predicting the carbon emissions of urban street vehicles is a current challenge in the field of urban transportation. This study proposed a new SAGE-GSAN model (Graph SAmple and aggreGatE - Graph Spatial Attention Network) to solve this problem. It combines graph neural networks with streetscape features and road network structure to predict the traffic carbon monoxide emissions at the street level. The method takes the street view images, the 5075 roads network structure in Wuhan and 19 street visual elements as the input features, and the carbon monoxide emissions obtained from the driving trajectories as the prediction data. The method achieved an experimental accuracy of 81.4% in predicting carbon monoxide emissions from street cabs. This study also compares the prediction results of traditional neural networks and analyzes the effects of different street-level features and graph convolution layers on the prediction accuracy. The results of this study show that the graph neural network and attention mechanism techniques could solve the fine-grained carbon emission prediction problem at the urban street level effectively. The model code is shared at the: https://github.com/zou9229/CO_Predict_Code.

Combating corruption on the frontlines: analyzing penalties for street-level bureaucrats in China

Penalties for corrupt acts are a vital component of integrity management systems within bureaucratic organizations, yet systematic inquiry into specific interventions is lacking. Drawing from the principal–supervisor–agent model, this article explores the criteria for imposing penalties on corrupt bureaucrats at the street level. We examine the impact of various factors on the severity of penalties for corruption, highlighting the influence of top-down anticorruption reform and local enforcement capacity. Utilizing text mining techniques, we build a comprehensive dataset containing 4025 cases of punishing corruption among grassroots officials in China from 2015 to 2023 and use multilevel analysis to explore these dynamics. Findings suggest that anticorruption reform directly led to an increase in the severity of penalties, but with substantial variation across practices. In particular, the analysis reveals a positive correlation between enforcement capacity and the severity of penalties. Moreover, while the relationship between harm and penalty severity represents a generalizable observation, significant disparities exist in the penalties imposed across various types of corruption. This study contributes to understanding the dynamics of multiple punitive measures and the factors leading to penalties imposed on corrupt street-level bureaucrats.

Resilience to climate change by improving air circulation efficiency and pollutant dispersion in cities: A 3D-UFO approach to urban block design

Urbanization presents significant challenges to air quality and climate resilience, necessitating pioneering urban design solutions to enhance air circulation and mitigate pollutants. This urgency intensifies in densely populated and rapidly evolving regions like Wuhan, China, where effective strategies are crucial for sustainable development. This study introduces an innovative 3D Urban Form Optimization (3D-UFO) methodology aimed at advancing urban block design configurations to improve urbanization quality. The 3D-UFO approach systematically addresses the multifaceted challenges of climate change and air quality degradation in rapidly urbanizing areas. Integrating GIS-based analysis for comprehensive Land-Use and Land-Cover Change (LULCC) evaluation with Computational Fluid Dynamics (CFD), our approach employs systematic exploration guided by established urban airflow study protocols. Robust metrics—Airspeed-Ratio (ASR) and Average-Age-of-Local-Air (ALA)—quantify the impact of diverse urban block design strategies on air-circulation efficiency and pollutant dispersion. Analysis across various urban scenarios, yielded by the proposed 3D-UFO approach, reveal significant variations in air-circulation efficiency at street and building levels (SBLs). Optimal urban air circulation achieves efficiency levels of 50–70 % when airflow aligns orthogonally across and parallel to streets. Adjusting street-level building heights, especially incorporating taller structures, boosts ventilation efficiency by 20–30 %, which is crucial for improving airflow dynamics in urban settings. Higher Height-to-Width (H/W) ratios (>5.5) yield a 218.5 % increase in ventilation in specific urban layouts. Notably, the synergy of street-aspect-ratio and building-height-ratio adjustments significantly enhance ASR and ALA, providing a quantitative foundation for sustainable urban development. This 3D-UFO methodology, fusing LULCC analysis, CFD simulations, and systematic exploration, emerge as a valuable framework for urban planners and designers. The study offers informed insights into urban sustainability challenges, demonstrating advancements in addressing environmental concerns and improving living conditions within densely populated environments.

Minding the gap between public administration curriculum and practice: The studio of public reasoning

It has always been a crucial question as to what kind of education should be provided to practitioners working at different levels of public administration, starting from the street level onwards, and what kind of interactions should be used to develop their competencies once in office. However, especially in the last 30 years, as we have entered a period of accelerating technological and institutional transformations, this issue has gained a dimension beyond the discussions on higher education and training for service. In addition to how public administration education will relate to practice as seen in traditional debates, new questions have emerged, such as how this engagement with practice can provide a perspective on institutional transformation, how those working in institutions can perceive their transformation processes more accurately, and ultimately, what kind of interaction can be achieved between those receiving public administration education and those working in public institutions. To answer these questions, an innovative model called “studio of public reasoning” was developed in 2009 for a university in public administration education in Turkey to create a platform for interaction with the external stakeholders of the university through an applied course. This Program has been implemented continuously for 15 years at the same university and more than 1000 students, about 100 public institutions of different scales and about 300 public administrators have benefited from the Program. In this paper, based on this experience, the gains of innovative teaching practices in public administration discipline in interacting with external stakeholders are critically evaluated through interviews with students, academics and administrators to assess whether alternatives are possible in establishing non-hierarchical external stakeholder interaction together with formal higher education for public administration.

Machine learning framework for high-resolution air temperature downscaling using LiDAR-derived urban morphological features

Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352 °K and MAE of 0.215 °K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.

Mathematical models for traffic-source PM2.5 dispersion in an urban street canyon considering the capture capability of roadside trees

Rapid urbanization increases the densely built-up blocks, the population and vehicles. Large amounts of particulate matter (PM), especially PM2.5 (PM with an aerodynamic diameter of 2.5μm or less), from vehicle exhaust are critical to human health. In typical street canyons in hot and humid regions, traffic-source PM usually diffuses to the densely built-up blocks through roadside trees. Roadside trees are a double-edged sword, serving as “guards” to absorb PM2.5 while may lead to PM2.5 gathering in street levels, thereby influencing the PM2.5 dispersion in the densely built-up blocks. To quantify the dispersion process, this study proposed traffic-source PM2.5 dynamic dispersion models considering the capture capability of roadside trees and built-up blocks based on the OSPM model. Due to the difficulty in obtaining the adsorption and deposition rate of the proposed models, the numerical simulations by ENVI-met software were used to solve and obtain the relationship between capture capability and characteristic index of roadside trees. Subsequently, The accuracy and effectiveness of the proposed traffic-source PM2.5 dynamic dispersion models were verified through field experimental data. Results show that the calculated PM2.5 concentration significantly linearly increased with the measured values with the determined coefficient (R2) of 0.98, and the first-order coefficient close to 1. It indicates that the proposed traffic-source PM2.5 dispersion model accurately quantified the impact of roadside trees on PM2.5 and its concentration dispersion process to the built-up blocks. This study provides suggestions for designing characteristic indexes of roadside trees and built-up blocks to improve the air quality of urban street canyons.

Assessment of air quality in the Philadelphia, Pennsylvania subway.

Subways are popular and efficient modes of transportation in cities. However, people are exposed to high levels of particulate matter (PM) in subways. Subway air quality in the United States has been investigated in a few cities, but data is lacking on simultaneous measurement of several pollutants, especially ultrafine particles (UFP) and black carbon (BC), in combination with different size fractions of PM. The goals of this study are to assess air quality in a belowground subway and compare it with outdoor ambient levels, to examine temporal variability of PM in the subway, and to analyze the correlation between PM and BC. Particulate matter of varying sizes (PM1, PM2.5, PM10), UFP, and BC were measured using DustTrak, nanoparticle detector, and micro aethalometer, respectively. Measurements were made at the belowground subway platform and the aboveground street level at 15th Street subway station in Philadelphia during summer 2022. Belowground mean PM1, PM2.5, and PM10 were 112.2 ± 61.3 µg/m3, 120 ± 65.5 µg/m3, and 182.1 ± 132 µg/m3, respectively, which were 5.4, 5.7, and 7.6 times higher than the respective aboveground street levels. The UFP lung deposited surface area (LDSA) (59.4 ± 36.2 µm2/cm3) and BC (9.5 ± 5.4 μg/m3) belowground were 1.7 times and 10.7 times higher than the aboveground. The pollutant concentration varied from day-to-day on both the locations. A higher positive correlation was found between the belowground BC and PM2.5 (r = 0.51, p < 0.05) compared to the aboveground (r = 0.16, p < 0.05). This study showed high levels of particulate matter exposure at a belowground subway station in Philadelphia. Particulate matter levels were about 5 to 8 times higher at belowground subway station than the corresponding aboveground street level. Higher levels were also observed for UFP lung deposited surface area (LDSA), while black carbon levels showed the highest concentration at the belowground level by a factor of ten compared to the aboveground level. The study shows the need for air quality management at belowground subways to reduce particulate matter exposure for the commuters.

Street Community-Level Urban Flood Risk Assessment Based on Numerical Simulation

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. Scientific and accurate assessment of waterlogging disaster losses is of scientific significance for the formulation of disaster prevention and mitigation measures and the guidance of post-disaster recovery and reconstruction. In this study, the SCS-CN hydrological model and GIS coupling numerical simulation method were used to simulate the inundation of urban waterlogging under four different rainfall return periods and to realize the visualization of the inundation range and waterlogging depth in Zhengzhou. At the same time, based on the numerical simulation results, the building is used as the basic assessment unit to construct a refined assessment framework for urban waterlogging risk at the street community level based on hazard, exposure, and vulnerability analysis. The refined risk assessment results have an important reference value for optimizing the working ideas of waterlogging control and providing a reference for local management departments to effectively deal with waterlogging disasters.

Does Being a HERO Really Make a Positive Difference to Police Officer Street Level Bureaucrats’ Well-Being?

This study examines the extent to which being a HERO (i.e., Hope, Efficacy, Resilience, and Optimism) impacts police officer Street Level Bureaucrats (SLBs) operational and organizational stress, and their subsequent perceived well-being. The sample comprised 220 Italian and 228 English SLBs, and hypotheses were tested using the Analysis for Moment Structures (AMOS) v.27 Structural Equation Modeling software. The findings depict that HERO explains approximately a fifth of SLBs’ organizational stress, and together, their variance accounted for approximately two-thirds of SLBs’ well-being. Finally, as one personal psychological resource for helping police officers cope with stress in the workplace, the findings indicate a need to upskill SLBs in HERO to better negotiate bureaucratic processes without becoming more susceptible to negative stress-related outcomes.

Can Street-Level Bureaucrats Assist with Material Resources? Naming, Trivializing and Privatizing Economic Abuse in Israel

ABSTRACT Street Level Bureaucrats’ (SLBs) contribution to social inequality was proposed to be best assessed by combining three analytical approaches: Discretion, Positioning, and Reproduction. Such a triple analytical framework may be required for understanding SLBs’ responses to Economic Abuse (EA) in Israel where formal law has not addressed EA while the prevalent welfare orientation denies the material resources required in responding to EA. Applying the triple framework in analyzing fifty-three interviews conducted with Israeli SLBs operating in state welfare organizations reveals the extent to which each analytical component is open for SLBs’ moral entrepreneurship, generating a sense of appropriate professional treatment. It develops a critical discussion of the conditions under which applying the triple framework of discretion, positioning, and reproduction is conducive.