Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications

What Works in Early Childhood Education Programs?: A Meta-Analysis of Preschool Enhancement Programs

Research Findings: This study uses data from a comprehensive meta-analytic database of early childhood education (ECE) program evaluations published between 1960 and 2007 in the United States to examine the incremental effects of adding enhancement program components to ECE programs on children’s cognitive abilities, pre-academic skills, behavioral, health, and socio-emotional outcomes. Preschool enhancement programs include parenting programs, skill-based curricula, and teacher professional development programming. Our findings suggest that the addition of parent programs and skill-based curricula to ECE programs can result in improvements to a range of children’s ECE outcomes leading to better school readiness. We found no differences in the impacts of ECE programs with or without additional professional development enhancements. Practice or Policy: Designing fully-developed parent programs by explicitly targeting parents, developing academically focused and skill-based curricula, and providing additional teacher professional development enhancements to existing ECE programs can have a substantial impact on a range of children’s ECE outcomes leading to better school readiness. Further research is needed in order to determine what conditions are essential to enhancement program success as well as what conditions have negligible effects on or inhibit children’s school readiness

Evaluating the End-User Experience of Private Browsing Mode

Nowadays, all major web browsers have a private browsing mode. However, the mode's benefits and limitations are not particularly understood. Through the use of survey studies, prior work has found that most users are either unaware of private browsing or do not use it. Further, those who do use private browsing generally have misconceptions about what protection it provides. However, prior work has not investigated \emph{why} users misunderstand the benefits and limitations of private browsing. In this work, we do so by designing and conducting a three-part study: (1) an analytical approach combining cognitive walkthrough and heuristic evaluation to inspect the user interface of private mode in different browsers; (2) a qualitative, interview-based study to explore users' mental models of private browsing and its security goals; (3) a participatory design study to investigate why existing browser disclosures, the in-browser explanations of private browsing mode, do not communicate the security goals of private browsing to users. Participants critiqued the browser disclosures of three web browsers: Brave, Firefox, and Google Chrome, and then designed new ones. We find that the user interface of private mode in different web browsers violates several well-established design guidelines and heuristics. Further, most participants had incorrect mental models of private browsing, influencing their understanding and usage of private mode. Additionally, we find that existing browser disclosures are not only vague, but also misleading. None of the three studied browser disclosures communicates or explains the primary security goal of private browsing. Drawing from the results of our user study, we extract a set of design recommendations that we encourage browser designers to validate, in order to design more effective and informative browser disclosures related to private mode

Evaluating the impact of integrated development: are we asking the right questions? A systematic review [version 2; referees: 2 approved, 1 approved with reservations]

Background: Emerging global transformations - including a new Sustainable Development Agenda - are revealing increasingly interrelated goals and challenges, poised to be addressed by similarly integrated, multi-faceted solutions. Research to date has focused on determining the effectiveness of these approaches, yet a key question remains: are synergistic effects produced by integrating two or more sectors?  We systematically reviewed impact evaluations on integrated development interventions to assess whether synergistic, amplified impacts are being measured and evaluated. Methods: The International Initiative for Impact Evaluation’s (3ie) Impact Evaluation Repository comprised our sampling frame (n = 4,339). Following PRISMA guidelines, we employed a three-stage screening and review process. Results: We identified 601 journal articles that evaluated integrated interventions. Seventy percent used a randomized design to assess impact with regard to whether the intervention achieved its desired outcomes. Only 26 of these evaluations, however, used a full factorial design to statistically detect any synergistic effects produced by integrating sectors. Of those, seven showed synergistic effects. Conclusions: To date, evaluations of integrated development approaches have demonstrated positive impacts in numerous contexts, but gaps remain with regard to documenting whether integrated programming produces synergistic, amplified outcomes. Research on these program models needs to extend beyond impact only, and more explicitly examine and measure the synergies and efficiencies associated with linking two or more sectors. Doing so will be critical for identifying effective integrated development strategies that will help achieve the multi-sector SDG agenda

The role of relationship types on condom use among urban men with concurrent partners in Ghana and Tanzania

Multiple concurrent partnerships are hypothesized to be important drivers of HIV transmission. Despite the demonstrated importance of relationship type (i.e. wife, girlfriend, casual partner, sex worker) on condom use, research on concurrency has not examined how different combinations of relationship types might affect condom use. We address this gap, using survey data from a sample of men from Ghana (n=807) and Tanzania (n=800) who have at least three sexual partners in the past three months. We found that approximately two-thirds of men's reported relationships were classified as a girlfriend. Men were more likely to use a condom with a girlfriend if their other partner was a wife compared to if their other partner was a sex worker (Ghana OR 3.10, 95% CI, 1.40, 6.86; Tanzania OR 2.34 95% CI 1.35, 4.06). These findings underscore the importance of considering relationship type when designing HIV prevention strategies in these settings

Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis

Drawing reliable inferences from data involves many, sometimes arbitrary, decisions across phases of data collection, wrangling, and modeling. As different choices can lead to diverging conclusions, understanding how researchers make analytic decisions is important for supporting robust and replicable analysis. In this study, we pore over nine published research studies and conduct semi-structured interviews with their authors. We observe that researchers often base their decisions on methodological or theoretical concerns, but subject to constraints arising from the data, expertise, or perceived interpretability. We confirm that researchers may experiment with choices in search of desirable results, but also identify other reasons why researchers explore alternatives yet omit findings. In concert with our interviews, we also contribute visualizations for communicating decision processes throughout an analysis. Based on our results, we identify design opportunities for strengthening end-to-end analysis, for instance via tracking and meta-analysis of multiple decision paths

Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT

ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as “the printing press”, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the “AI arms race”, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well-being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research

Human resource management in the age of generative artificial intelligence::Perspectives and research directions on ChatGPT

ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as “the printing press”, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the “AI arms race”, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well‐being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research