Polygenic scores (PGS) have emerged as a promising tool for understanding the genetic underpinnings of physical fitness traits, including aerobic fitness (also called cardiorespiratory fitness), muscular fitness and adiposity, and neuromotor performance (including agility/speed-related parameters). However, the predictive utility of PGS for physical fitness traits is unclear. To address this knowledge gap, we conducted a systematic review to critically evaluate the current body of literature on the association between weighted and unweighted PGS models, and various fitness-related phenotypes. Following PRISMA guidelines, we identified 67 studies published prior to May 2025 that examined 74 weighted and 39 unweighted PGS models in relation to cardiovascular, respiratory, muscular and adiposity, and/or neuromotor performance-related parameters. We classified body composition along with muscular fitness in the same category, because of the close correlation of these parameters. Our evaluation highlights key genetic components implicated in fitness traits, the methodological heterogeneity across studies, and the limitations of current PGS models. While PGSs offer insights into the genetic architecture of physical fitness, their practical application remains constrained by population specificity, polygenic complexity, and environmental interactions. We discuss the implications for personalized interventions and future research directions to enhance the predictive utility of PGS for physical fitness.
This article examines the construction of international students, particularly Punjabi international students, in Canadian policies and mainstream media discourses post–COVID-19. It then juxtaposes these representations with the perspectives of students themselves, as captured through a survey with Punjabi international students transitioning out of British Columbia colleges and teaching universities. Our analysis reveals the fraught dialectic between the policy landscape, media discourses, and Punjabi international students’ trajectories. On a policy level, when international students are perceived to hold high economic value with low social costs, they are welcomed; when that perception shifts, they are “Othered.” Similarly, media discourses represent Punjabi international students in complex and often contradictory ways as victims, system abusers, commodities, and strains on infrastructure. In contrast, Punjabi international students largely see themselves as young people striving to build better futures, buying into the “Canadian dream” while simultaneously navigating processes that render them vulnerable to exploitation and Othering.
In recent years, Canada’s international education system has undergone substantial restructuring, significantly altering pathways from post-secondary education to employment and permanent residency. This paper examines Punjabi international students’ aspired post-graduation trajectories in British Columbia during a period of rapid policy change. While existing work on Punjabi international students has documented recruitment pathways and in-program experiences, less attention has been paid to what happens at the point of exit, particularly under conditions of rapid policy changes. Drawing on mixed methods data, including a survey of 212 students and 14 in-depth interviews across four public post-secondary institutions, we analyze how students navigate shifting eligibility requirements and labour market expectations at the point of exit from post-secondary education. Using the lenses of governmentality and biopower, we conceptualize the post-graduation transition as a governed site where students are continually sorted through changing administrative criteria and where responsibility for managing volatility is displaced onto individuals. The findings reveal that students’ aspired post-graduate decisions and trajectories are influenced more by changing immigration and eligibility rules than by job prospects because these policies often reclassify their work/credentials as (in)eligible. By centering post-graduation transition as a critical yet underexamined site of governance in the education–migration system, this study contributes to scholarship on international education and migration and underscores the need for more stable and just policy frameworks that better align international education, labour market outcomes, and immigration objectives.
This paper investigates domain-adversarial transfer learning for small-area crime forecasting around the Sur-rey-Langley SkyTrain (SLS) expansion in British Columbia, Canada. A two-stage pipeline is proposed: it first trains a Domain-Adversarial Neural Network (DANN) on multi-city data (Surrey and Coquitlam) to learn domain-invariant representations from seasonal-average crime outcomes, coupled with rich covariates: socioeconomic and demographic indicators, transit accessibility, and region-popularity. The pipeline then freezes the extracted features and fits a LightGBM model to predict spatiotemporal crime patterns in the target city (Langley). Timeforward, cross-city evaluations show that DANN-derived features yield better generalization than city-specific models, pooled nonadversarial baselines, and LightGBM trained on raw inputs. Finally, scenario-based forecasts were executed for Langley under simulated post-SLS transit accessibility, producing fine-grained risk maps that highlight potential shifts near the future corridor and station areas. The results indicate that pairing adversarially learned, domain-robust representations with a strong tabular learner offers an effective strategy for transferring knowledge across municipalities and forecasting infrastructure-induced changes in urban safety. A key contribution of this work lies in demonstrating that such a transfer-learning pipeline can support planning decisions for data-scarce municipalities preparing for major transit expansions, where no local historical analogue exists.
In this paper, we discuss the use of voting and linearly weighted algorithms on a set of base classifiers for a binary classification problem to enhance the predictive accuracy. We introduce a generalization of the confusion matrix concept to facilitate the study of the majority voting and other voting algorithms. We prove that, under conditions that are common in practice, the linearly weighted combined classifiers are the same as the majority voting classifier. We illustrate these concepts using a popular data set in machine learning.
