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An aspect of citation behavior, which has received longstanding attention in research, is how articles’ received citations evolve as time passes since their publication (i.e., citation ageing). Citation ageing has been studied mainly by the formulation and fit of mathematical models of diverse complexity. Commonly, these models restrict the shape of citation ageing functions and explicitly take into account factors known to influence citation ageing. An alternative—and less studied—approach is to estimate citation ageing functions using data-driven strategies. However, research following the latter approach has not been consistent in taking into account those factors known to influence citation ageing. In this article, we propose a model-free approach for estimating citation ageing functions which combines quantile regression with a non-parametric specification able to capture citation inflation. The proposed strategy allows taking into account field of research effects, impact level effects, citation inflation effects and skewness in the distribution of cites effects. To test our methodology, we collected a large dataset consisting of more than five million citations to 59,707 research articles spanning 12 dissimilar fields of research and, with this data in hand, tested the proposed strategy.

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.

The use of active transportation (AT), such as walking, cycling, or even public transit, as a means of transport offers an opportunity to increase youth physical activity and improve health. Despite the well-known benefits of AT, there are environmental and social variables that converge on the AT experiences of low-income youth and youth of color (YOC) that have yet to be fully uncovered. This study uses an intersectional framework, largely focusing on the race-gender-class trinity, to examine youth AT within a context of transportation inequity. Theoretically guided by the Ecological Model of Active Transportation, focus groups were completed with two groups of girls (15 participants) and two groups of boys (nine participants) ranging between the ages of 12-15 years who lived within the Washington D.C. area. This research found race, gender, and class to be inhibitors of AT for both boys and girls, but with more pronounced negative influences on girls.

Amy L. Lewin, Kevin Roy, Family Science, individual and structural inequalities deriving from traumatic experiences among immigrant Latino youth

Caryn Bell, African American Studies, examines the effects of macro-level structural racism on population health

Collaborative science brings together diverse stakeholders to share knowledge and form networks that in turn can be foundational to policies and practices to increase socio-ecological resilience. In this article, we present results from a collaborative science project that employed collaborative learning methods to develop a network of local, regional, state and academic stakeholders. These stakeholders had little social interaction prior to the project and represented a diversity of views, positions and responsibilities. They shared in common a concern for the effects of climate change on a coastal socio-ecological system and the desire to reduce vulnerabilities and enhance resilience. Through ethnographic and survey methods, we found that collaborative science and learning promoted the exchange of cultural and environmental knowledge and expertise among individuals who previously had no sustained interaction. Stakeholders perceived these exchanges as worthwhile in that they allowed individuals to express viewpoints and share knowledge and expertise, which was seen to have the potential to increase socio-ecological resilience. Our results suggest that social networks can emerge from collaborative science and learning projects and can become formally organized and help foster opportunities to enhance socio-ecological resilience.

John Haltiwanger looks at the effect of housing market shocks on young businesses and start-ups

Purpose Limited research has examined maternal experiences of racial/ethnic discrimination in relation to child cardiometabolic health. In this study, we investigated whether maternal experiences of ethnic discrimination were associated with cardiometabolic risk in Hispanic/Latino youth several years later. Methods Our sample included 1146 youth (8–16 years) from the Study of Latino Youth (2012–2014), who were children of the Hispanic Community Health Study/Study of Latinos participants (2008–2011). We used regression models to examine the prospective associations between maternal report of ethnic discrimination in relation to her child's  body mass index  (BMI) z-score,  metabolic syndrome  score (MetS), and high sensitivity  C-reactive protein  (hsCRP) levels 2 years later. Results Maternal ethnic discrimination was associated with youth hsCRP, but not BMI or MetS (P -values >.05). Adjusting for age, nativity, and national background, maternal ethnic discrimination was associated with higher (log) hsCRP levels (β = 0.18, 95% CI = 0.04 to 0.32) in children. This association was robust to adjustment for maternal and household characteristics (β = 0.17, 95% CI = 0.04 to 0.31), as well as  maternal depression  and maternal BMI. Conclusions Maternal ethnic discrimination is associated with inflammation among Hispanic/Latino youth, and not BMI z-score or MetS. Studies are needed to address temporality and pathways.

"Unwanted fertility," especially in rural regions, should be top priority

O besity rates in the U.S. are associated with area-level, food-related characteristics. Studies have previously examined the role of structural racism (policies/practices that advantaged White Americans and deprived other racial/ethnic minority groups), but racial inequalities in socioeconomic status (SES) is a novel indicator. The aim of this study is to determine the associations between racial inequalities in SES with obesity and obesogenic environments. Data from 2007⁻2014 County Health Rankings and 2012⁻2016 County Business Patterns were combined to assess the associations between relative SES comparing Blacks to Whites with obesity, and number of grocery stores and fast food restaurants in U.S. counties. Random effects linear and Poisson regressions were used and stratified by county racial composition. Racial inequality in poverty, unemployment, and homeownership were associated with higher obesity rates. Racial inequality in median income, college graduates, and unemployment were associated with fewer grocery stores and more fast food restaurants. Associations varied by county racial composition. The results demonstrate that a novel indicator of structural racism on the county-level is associated with obesity and obesogenic environments. Associations vary by SES measure and county racial composition, suggesting the ability for targeted interventions to improve obesogenic environments and policies to eliminate racial inequalities in SES.