Public Policy Domain Research Articles

Are Brothels Markets? Situating Consumer Research at the Nexus of the Essential, the Embarrassing and the Exception: A Viewpoint from Sociological Traditions

This essay critiques the trend in consumer research that deliberately excludes subjects considered exceptional. Drawing from multiple traditions in sociological literature, we analyse the interaction of factors that create persistent paradoxes in social imaginaries - paradoxes that have also impacted the academic community. While the majority of scholarly publications tend to take a reductionist view of consumer concerns, the vulnerabilities faced by queer groups and sex workers are largely ignored, with sex workers being pushed to the absolute margins. The relentless pursuit of tokenism, quantified by novel contributions or citations, has further marginalized invisible narratives of hope and resilience. The article advocates for a more inclusive approach in the development of new areas of consumer research, particularly in the domains of macro marketing, public policy, and transformational consumer research.

Foreword for the Collection of Papers from the Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy

The 2022 Workshop on the Analysis of Census Noisy Measurement Files and Differential Privacy brought together research experts from many domains of social sciences, demography, public policy, statistics, and computer science to address key challenges in the use of the differentially private Census noisy measurement files to support social research and policy decisions.

Regional climate risks and government education expenditure: evidence from China

Faced with the impact of climate risks, what responses do local governments make, particularly in the domain of social public policy? This study uses provincial panel data from China from 2009 to 2020 to investigate how climate risks affect local government education expenditure and its mechanisms. Analysis using spatial econometric models reveals the following. First, local climate risks significantly increase government education expenditure. This conclusion remains consistent across different proxy variables, model specifications, and estimation methods. Additionally, there is a spatial dependency on local government education expenditure, meaning that educational spending in neighboring areas positively influences the education expenditure of a specific location. Second, further mechanism analysis shows that climate risks lead to credit easing and drive the digital economic transformation of a location, thereby increasing its education expenditure. Third, considering the heterogeneity in the spatial dimension, it is found that this positive impetus exists only in the economically more developed eastern region. This study provides insights into understanding how the Chinese government responds to the impact of climate risks and its fiscal policy choices.

Preliminary evidence for counterproductive work behavior as a workplace manifestation of science skepticism

Although media has called attention to overly skeptical attitudes towards science in public policy, medicine, and other domains, the effects of science skepticism in the workplace are largely unexplored. Consequently, we investigated whether science skepticism correlated with counterproductive workplace behaviors (CWB). Data from 120 self-reports and 57 supervisor-subordinate dyads shows that some forms of science skepticism relate to self- and supervisor-rated CWB, even controlling for negative affectivity. Furthermore, the MODE model supports the involvement of individual differences in the activation of an attitude, so we also tested if hostile attributional style and learning goal orientation moderated this relationship. Data from 148 participants recruited from an online panel shows a positive relationship between science skepticism and CWB, and that this relationship was strengthened when participants held hostile attribution styles. This moderation was weakened by learning goal orientation. Altogether, our two small online studies provide initial evidence for detrimental workplace outcomes associated with science skepticism and are intended to invigorate future research on this topic.

Artificial intelligence in biology and medicine, and radioprotection research: perspectives from Jerusalem.

While AI is widely used in biomedical research and medical practice, its use is constrained to few specific practical areas, e.g., radiomics. Participants of the workshop on "Artificial Intelligence in Biology and Medicine" (Jerusalem, Feb 14-15, 2023), both researchers and practitioners, aimed to build a holistic picture by exploring AI advancements, challenges and perspectives, as well as to suggest new fields for AI applications. Presentations showcased the potential of large language models (LLMs) in generating molecular structures, predicting protein-ligand interactions, and promoting democratization of AI development. Ethical concerns in medical decision making were also addressed. In biological applications, AI integration of multi-omics and clinical data elucidated the health relevant effects of low doses of ionizing radiation. Bayesian latent modeling identified statistical associations between unobserved variables. Medical applications highlighted liquid biopsy methods for non-invasive diagnostics, routine laboratory tests to identify overlooked illnesses, and AI's role in oral and maxillofacial imaging. Explainable AI and diverse image processing tools improved diagnostics, while text classification detected anorexic behavior in blog posts. The workshop fostered knowledge sharing, discussions, and emphasized the need for further AI development in radioprotection research in support of emerging public health issues. The organizers plan to continue the initiative as an annual event, promoting collaboration and addressing issues and perspectives in AI applications with a focus on low-dose radioprotection research. Researchers involved in radioprotection research and experts in relevant public policy domains are invited to explore the utility of AI in low-dose radiation research at the next workshop.

The Relationship Between Public Policy in the Domain of Public Economic Law Policy

The objective of this research was to analyze the relationship between public policy in the domain of public economic law policy. The primary policymakers in the field of public economics, in light of ethical schools of thought, aim to distinguish between the current state and desired goals. They strive to bridge the gap between what is and what should be by utilizing various tools. On the other hand, the successful implementation of these processes depends on the continuous evaluation of all components of these two main and significant factors, which is typically carried out based on various elements such as the governing principles of public law and economic policy. Muslim jurists generally extract these principles by referring to numerous Quranic verses and Hadiths or by utilizing different jurisprudential approaches. In addition to these legal discussions, various economic schools have also structured their systems of policy evaluation using their own targeted and diverse criteria regarding moral valuation. The findings indicate that choosing any of the approaches—deontology, virtue ethics, or ethical economics—has specific implications for evaluating the policies of different legal and economic schools. Muslim economists, as well, have proposed other methods, aside from normative ethical theories, to derive the governing principles of Islamic economic policies. They have adopted frameworks of Islamic economic value systems based on referencing Quranic verses, Hadiths, or employing jurisprudential approaches to extract these principles.

Appraising systematic reviews: a comprehensive guide to ensuring validity and reliability.

Systematic reviews play a crucial role in evidence-based practices as they consolidate research findings to inform decision-making. However, it is essential to assess the quality of systematic reviews to prevent biased or inaccurate conclusions. This paper underscores the importance of adhering to recognized guidelines, such as the PRISMA statement and Cochrane Handbook. These recommendations advocate for systematic approaches and emphasize the documentation of critical components, including the search strategy and study selection. A thorough evaluation of methodologies, research quality, and overall evidence strength is essential during the appraisal process. Identifying potential sources of bias and review limitations, such as selective reporting or trial heterogeneity, is facilitated by tools like the Cochrane Risk of Bias and the AMSTAR 2 checklist. The assessment of included studies emphasizes formulating clear research questions and employing appropriate search strategies to construct robust reviews. Relevance and bias reduction are ensured through meticulous selection of inclusion and exclusion criteria. Accurate data synthesis, including appropriate data extraction and analysis, is necessary for drawing reliable conclusions. Meta-analysis, a statistical method for aggregating trial findings, improves the precision of treatment impact estimates. Systematic reviews should consider crucial factors such as addressing biases, disclosing conflicts of interest, and acknowledging review and methodological limitations. This paper aims to enhance the reliability of systematic reviews, ultimately improving decision-making in healthcare, public policy, and other domains. It provides academics, practitioners, and policymakers with a comprehensive understanding of the evaluation process, empowering them to make well-informed decisions based on robust data.

Design Thinking in Public Policy Design: A Typological Study

In recent years, design has emerged as a method for shaping public policies and services. This article employs a typological research approach to analyze the application of design thinking in the field of public policy. Its aim is to elucidate the developmental trajectory of design thinking, assist designers in gaining a better understanding of the unique context of public policy, and promote the further evolution of design thinking in the domain of public policy. Through literature analysis and comprehension, this article is built upon the foundation of public sector policy labs. It constructs an analytical framework based on two dimensions, institutional nature and participation stages, categorizing public sector innovation labs in the field of public policy into three types: exploratory labs, executive labs, and collaborative labs. It also summarizes the classification criteria and characteristics of each type of lab. Furthermore, the article explores the inherent logical relationships between different types of labs, offering a systematic and scientific analysis to facilitate the integration of design thinking into public policy.

Population-level policy recommendations for the prevention of disordered weight control behaviors: A scoping review.

The aim of this scoping review was to identify recommendations and gaps in knowledge surrounding the prevention of disordered weight control behaviors (DWCBs) through policy. A search was conducted in several databases to identify English language articles that described an active policy, recommendation, guideline, or educational curriculum that could be implemented by governments or regulatory bodies to prevent DWCBs or related constructs (e.g., weight stigma, body dissatisfaction). Two researchers independently screened articles with oversight from a third researcher. Data were extracted from the final sample (n = 65) and analyzed qualitatively across all articles and within the domains of education, public policy, public health, industry regulation, and media. Only a single empirical evaluation of an implemented policy to reduce DWCBs was identified. Over one-third of articles proposed recommendations relating to industry regulation and media (n = 24, 36.9%), followed by education (n = 21, 32.3%), public policy (n = 19, 29.2%), and public health (n = 10, 15.4%). Recommendations included school-based changes to curricula, staff training, and anti-bullying policies; legislation to ban weight discrimination; policies informed by strategic science; collaboration with researchers from other fields; de-emphasizing weight in health communications; diversifying body sizes and limiting modified images in media; and restricting the sale of weight-loss supplements. The findings of this review highlight gaps in empirically evaluated policies to reduce DWCBs but also promising policy recommendations across several domains. Although some policy recommendations were supported by empirical evidence, others were primarily based on experts' knowledge, highlighting the need for greater research on population-level DWCBs prevention through policy. Our scoping review of the evidence on policies for the prevention of disordered weight control behaviors identified several recommendations across the domains of education, public policy, public health, and industry regulation and media. Although few empirical investigations of implemented policies have been conducted, expert recommendations for policies to prevent disordered weight control behaviors among populations are plentiful and warrant future consideration by researchers and policymakers alike.

Higher education and research in the Brexit policy process

ABSTRACT This article on post-Brexit policies for higher education and research suggests that these public policy domains are characterised by their intellectual independence from the state or market. The authors see a legacy of the UK’s historical relationship with the EU in its treatment of the two epistemically linked domains, reflecting institutional differences. UK governments have generally been wary of EU involvement in higher education and supportive of research collaboration. Post-Brexit evidence suggests that the UK has been purposively ‘de-Europeanising’ higher education for the supposed gains of marketised international policy. But the UK is also a victim of its overarching Brexit policy, which risked failing to secure associate status for the UK in the EU’s world famous Horizon Europe science programme. The article explores the question of whether Brexit caused divergence in these sectors or whether it provided the opportunity for the UK government to solidify an already semi-autonomous policy trajectory.

Authentic talent development in sociocultural context: interdisciplinary perspectives

PurposeThis paper aims to describe how the multidisciplinary studies in the special issue expand and enrich the framework of authentic talent development in sociocultural context.Design/methodology/approachThe study presents a framework of authentic talent development in sociocultural context and identifies key themes from the seven interdisciplinary studies of the special issue to enrich and expand our understanding of sociocultural barriers to authentic talent development for individuals from low-income and minoritized communities, including women.FindingsThe studies included in the special issue illustrate several ways in which social identity ascription truncates the talent trajectory of individuals from low-income and minoritized groups. They also show how ascription processes can be mitigated through policy, reformed organizational structure and practice and growth fostering relationships.Practical implicationsThe findings of this paper have implications for developing strategies to mitigate social identity ascription in talent development in the domains of public policy, schooling and the workplace.Originality/valueThe special issue brings together contributions from multiple disciplines to holistically understand the nature of social identity ascription in talent development across the life course. Collectively, the contributions identify a number of complimentary tools at several levels that might allow effective mitigation of social identity ascription processes, facilitating authentic talent development for individuals from low-income and minoritized communities.

Policy diffusion theory, evidence-informed public health, and public health political science: a scoping review

ObjectivesOur aim was to synthesize published scholarship that applies policy diffusion—a theory of the policy process that considers the interdependence of government-level public health policy choices. We paid particular attention to the role of scientific evidence in the diffusion process, and to identifying challenges and gaps towards strengthening the intersection of public health, public policy, and political science.MethodsWe systematically searched 17 electronic academic databases. We included English-language, peer-reviewed articles published between 2000 and 2021. For each article, we extracted the following information: public health policy domain, geographic setting, diffusion directions and mechanisms, the role of scientific evidence in the diffusion process, and author research discipline.SynthesisWe identified 39 peer-reviewed, primary research articles. Anti-smoking and tobacco control policies in the United States (n = 9/39) were the most common policy domain and geographic context examined; comparatively fewer studies examined policy diffusion in the Canadian context (n = 4/39). In terms of how policies diffuse, we found evidence of five diffusion mechanisms (learning, emulation, competition, coercion, and social contagion), which could moreover be conditional on internal government characteristics. The role of scientific evidence in the diffusion process was unclear, as only five articles discussed this. Policy diffusion theory was primarily used by public policy and political science scholars (n = 19/39), with comparatively fewer interdisciplinary authorship teams (n = 6/39).ConclusionPolicy diffusion theory provides important insights into the intergovernmental factors that influence public health policy decisions, thus helping to expand our conceptualization of evidence-informed public health. Despite this, policy diffusion research in the Canadian public health context is limited.

THE ROLE OF THE COURTS IN THE PUBLIC POLICY DOMAIN IN SOUTH AFRICA

During the pre-democratic constitutional dispensation, South African public law was marked by the pre-eminence of the executive and the legislature in a parliamentary system of government. The courts were generally loath to review matters related to policy. The adoption of the interim and subsequently the final constitutions changed the position by making the Constitution of the Republic of South Africa, 1996 the supreme law of the country, with separation of powers between the three branches of government. The approach of the courts has changed noticeably from restraint to activism. Their role in reviewing policies has been steadily growing. They are now thrust into the formulation of government or public policy, which traditionally fell within the ambit of the executive branch of government. This new-found role of the courts has led to concerns that the judiciary’s constant interference in the policy domain borders on violating the hallowed principle of separation of powers. The central argument of this article is that the courts’ intervention in the public policy domain, if not handled with caution, has the potential to undermine the principle of separation of powers implicit in the South African Constitution.

Explainable machine learning for public policy: Use cases, gaps, and research directions

Abstract Explainability is highly desired in machine learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with generic explainability goals without well-defined use cases or intended end users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., Amazon Mechanical Turk). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use cases of explainable ML within public policy problems. For each use case, we define the end users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is (a) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and (b) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

The Impact of Corporate Social Responsibility and Innovative Strategies on Financial Performance

The article aims to appraise the role of Corporate Social Responsibility (CSR) and innovation strategies as leverages of a company’s financial performance. The theoretical and empirical statement of this link aims to reinforce the importance of these strategical options in both the managerial and the public policy domain. Shedding light on the economic return of these practices will help managers make better strategic decisions. Policy makers will also grasp the required evidence to encompass CSR in policy packages. To address the research question, data were collected from the Thomson Reuters Eikon Datastream covering the 1000 largest companies listed on the stock exchange worldwide. Thereafter, hierarchical linear regressions were performed to produce the econometric results. Two time frames (2015–2019) were compared to address time–space trends. Enrolling in CSR activities entails additional costs which can undermine the company’s financial performance if not properly supported by public policies. Combining CSR and innovation appears to be the best strategy for companies seeking improvements in their financial performance while being socially responsible. The contribution of this study is threefold: first, the analysis covers the largest thousand firms in operation worldwide; secondly, the econometric results demonstrate that combining CSR with innovation positively impacts financial performance; and lastly, the time comparison evidences a positive but slow evolution in CSR adoption. The article provides an applied perspective, of use both for managers and policy makers, as to how they should approach and disseminate involvement in these types of activities.

“I did not know that there were problems”: government officials’ blame avoidance strategies in the Life Esidimeni Arbitration Hearings

Abstract The travesty of the Life Esidimeni project in South Africa, which claimed the lives of 144 mental health users at psychiatric facilities in Gauteng between 2016 and 2017, is multifaceted. One facet involves strategies of blame avoidance designed to escape liability for the deaths that were expressed by the former Member of the Executive Council for Health and other public health officials during the Life Esidimeni Arbitration Hearings. These hearings were broadcast on state television between October 2017 and 2018, and eight samples from the hearings were analysed for specific blame avoidance strategies. Following the principles of qualitative discourse analysis, this paper extends research on blame avoidance behaviour in the public administration and policy domain, exploring three key officials’ micro- and macro-level choices of blame avoidance in the context of the arbitration hearings to develop a more comprehensive account of these strategies. A public hearing is a discourse setting that is reactionary in nature, and our findings on the micro-level, a neglected dimension of research on blame avoidance behaviour, extend our understanding of these behaviours. We propose that at least two continua for blame avoidance are relevant in this setting.

Relationship between the actors and the environment: an analysis of positive and negative feelings about an entrepreneurial ecosystem

Objective of the study – To analyse the positive and negative feelings regarding the domains of an entrepreneurial ecosystem. Design/methodology/approach – The present work is characterized as exploratory and descriptive in relation to the procedures as field research, and the approach is qualitative. Findings – Most of the feelings regarding the entrepreneurial ecosystem in the study are negative (30 feelings). However, there are positive perceptions (14 feelings) about the arrangement. This can be seen through the importance of the people, actors and entrepreneurs in the ecosystem, as they act as agents in transforming actions. Concerning the negative aspects, the Public Policy and Culture domains also contain several indicators, mainly for the search for incentives and policies to disseminate entrepreneurship in the ecosystem. Practical & Social implications of the Study – This work sought to collaborate with studies in the field of entrepreneurship, specifically, in the domains of the ecosystem. Its strength lies in its theoretical and practical relevance, guiding entrepreneurs towards other specific aspects of the entrepreneurial ecosystem. Originality – The study is original due to the environment in which it was conducted, unlike other studies on this theme, and the local arrangement had not yet been considered. Furthermore, regarding similar studies conducted in Brazil, few studies were found approaching this theme of the entrepreneurial ecosystem (Capes, 2020), which indicates the possibility of exploring this theme.

ИЗУЧЕНИЕ ВЗАИМОСВЯЗИ МЕЖДУ НОРМАТИВНОЙ ИДЕНТИЧНОСТЬЮ И СТРАТЕГИЧЕСКИМИ РЕЗУЛЬТАТАМИ

This study aims to extend the concept of organizational identity, the soul of organizational culture, and its five characterizing attributes, namely, purpose, philosophy, priorities, processes, and projections, to the regulatory space. It has been done through a thematic review of previous articles. Although a few academic articles in the public policy arena have dealt with one or two of these attributes, there is a lack of a holistic evaluation of the regulatory framework covering all these five attributes of regulatory identity. After building an understanding of how these elements have evolved in public policy and regulation domains, we have hypothesized that processes and projections of the regulatory framework need to be aligned with the core regulatory identity (constituted by purpose, philosophy, and priorities) for achieving strategic outcomes. On the other hand, a change in the long-term strategy should trigger a reassessment of organizational priorities followed by the realignment of the core regulatory culture to guide decision-making and organizational actions. Finally, since the regulatory processes or practices, both internal and external, are the actions that keep the regulatory identity alive, therefore, they should be aligned with the core identity and culture of the regulatory framework.

Auditing and Debugging Deep Learning Models via Flip Points: Individual-Level and Group-Level Analysis

Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Nevertheless, they are consistently utilized in many applications, consequential to humans’ lives, usually because of their better performance. Therefore, there is a great need for computational methods that can explain, audit, and debug such models. Here, we use flip points to accomplish these goals for deep learning classifiers used in social applications. A trained deep learning classifier is a mathematical function that maps inputs to classes. By way of training, the function partitions its domain and assigns a class to each of the partitions. Partitions are defined by the decision boundaries which are expected to be geometrically complex. This complexity is usually what makes deep learning models powerful classifiers. Flip points are points on those boundaries and, therefore, the key to understanding and changing the functional behavior of models. We use advanced numerical optimization techniques and state-of-the-art methods in numerical linear algebra, such as rank determination and reduced-order models to compute and analyze them. The resulting insight into the decision boundaries of a deep model can clearly explain the model’s output on the individual level, via an explanation report that is understandable by non-experts. We also develop a procedure to understand and audit model behavior towards groups of people. We show that examining decision boundaries of models in certain subspaces can reveal hidden biases that are not easily detectable. Flip points can also be used as synthetic data to alter the decision boundaries of a model and improve their functional behaviors. We demonstrate our methods by investigating several models trained on standard datasets used in social applications of machine learning. We also identify the features that are most responsible for particular classifications and misclassifications. Finally, we discuss the implications of our auditing procedure in the public policy domain.

Strategies to enhance the success of mid-career nurse scientists

BackgroundThe mid-career nurse scientist, defined as an associate professor with/without tenure, is often faced with a multitude of challenges and opportunities PurposeThis paper shares strategies to assist mid-career scientists as they juggle required career demands and navigate the mid-career phase in pursuit of the rank of full professor. MethodA review of the literature was performed on mid-career nurse scientists. DiscussionA combination of increased research responsibilities, increased institutional teaching and service demands, and dwindling support can result in a sense of overwhelm and burnout. The mid-career nurse scientist must balance several balls in the air at one time to remain successful. ConclusionStrategies aligned with the Ecological Framework, focus on intrapersonal, interpersonal, institutional, organizational, and public policy domains to provide a wide scope of strategies that target the mid-career scientist and engage the larger nursing community.