Income Inequality Predicts Competitiveness and Cooperativeness at School

Competitiveness and cooperativeness are important predictors of social and learning outcomes at school. Drawing on evidence suggesting that contexts with high income inequality foster an ethos of competitiveness and inhibit cooperativeness in the economic environment, we examine whether income inequality is also associated with more competitiveness and less cooperativeness in the academic environment. We conducted four preregistered studies to test this idea. In Study 1, analysis of the OECD PISA 2018 dataset (≈500,000 15-year-old students from 75 countries) revealed that students from economically unequal countries perceive their schoolmates as more competitive and less cooperative. In Study 2a-2b, analysis of the PISA 2003 (250,000+ students from 38 countries) and PISA 2000 (75,000+ students from 32 countries) datasets revealed that students from unequal countries are themselves more competitive and, surprisingly, also more cooperative. Follow-up analyses resolved this apparent paradox, showing that students from unequal countries are oriented towards instrumental rather than intrinsic cooperativeness (i.e., using cooperation as a strategic tool to achieve academic success rather than for the enjoyment of the activity itself). Study 3 offers a conceptual experimental replication (≈850 young adults imagining going back to school) and indicates that induced income inequality (i) increases perceived competitiveness, (ii) decreases perceived cooperativeness, (iii) prompts an orientation towards competitiveness, and (iv) prompts an orientation towards instrumental rather than intrinsic cooperativeness. Results are discussed in relation to the multidisciplinary literatures on the psychology of income inequality, the selective function of school systems, coopetition, self-determination, and cooperative learning.

Explicating Exact versus Conceptual Replication

What does it mean to replicate an experiment? A distinction is often drawn between ‘exact’ (or ‘direct’) and ‘conceptual’ replication. However, in recent work, Uljana Feest argues that the notion of replication in itself, whether exact or conceptual, is flawed due to the problem of systematic error, and Edouard Machery argues that, although the notion of replication is not flawed, we should nevertheless dispense with the distinction between exact and conceptual replication. My plan in this paper is to defend the value of replication, along with the distinction between exact and conceptual replication, from the critiques of Feest and Machery. To that end, I provide an explication of conceptual replication, and distinguish it from what I call ‘experimental’ replication. On the basis, then, of a tripartite distinction between exact, experimental and conceptual replication, I argue in response to Feest that replication is still informative despite the prospect of systematic error. I also rebut Machery’s claim that conceptual replication is fundamentally confused and wrongly conflates replication and extension, and in turn raise some objections to his own Resampling Account of replication.

Addressing Missing Data in Untargeted Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication

Missing data is a significant issue in metabolomics that is often neglected when conducting data pre-processing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatog-raphy (GC×GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion—the first description and evaluation of this strategy—and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approach-es (Bayesian principal components analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.

Addressing Missing Data in Untargeted Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Meth-ods on Experimental Replication

Missing data is a significant issue in metabolomics that is often neglected when conducting data pre-processing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metab-olomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatog-raphy (GC×GC) data sets. We also present these goals in the context of experimental replication whereby imputation is con-ducted in a within-replicate-based fashion—the first description and evaluation of this strategy—and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approach-es (Bayesian principal components analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially im-portant features in downstream analyses for biomarker discovery.

Produção de bioenergia, segurança ambiental e desenvolvimento econômico

O presente artigo propõe debater acerca da relação entre segurança ambiental, conservação dos biomas, produção de material para bioenergia e gestão estruturada de uso da terra em propriedades rurais para conservação dos solos e da bacia hidrográfica. Para isso, propõe-se o uso da metodologia denominada Corredores Florestais Ecológicos Econômicos (CFEE). Para garantir seu sucesso metodológico, é preciso que sejam, de fato, corredores ideais capazes de resgatar espaços ecológicos econômicos florestais. Assim, antes de sua construção é ideal que se obedeçam aos diversos postulados pedológicos, geomorfológicos, agrícolas e biológicos, denominados condições ideais e resumidos através dos Desenhos de Uso Optimal do Território (DUOT). Somente pós obedecer estas condições, é possível de fato se construir tais CFEE, buscando seu máximo sucesso, que pode ser verificado através da replicação experimental proposta, realizada por Camargo (2018), confirmando que os pressupostos teóricos aqui apresentados mostram resultados de fato positivos. Como conclusão, pode-se afirmar que os modelos aqui discutidos integram uma visão de viabilidade ecológica e econômica entre plantios florestais ecológicos e econômicos, com conservação do bioma e da circulação hídrica, com o uso de agricultura intensiva para a produção de biomassa, madeiras de lei, frutos e fármacos com vista a eco sustentabilidade das bacias hidrográficas. Palavras-chave: Corredores Florestais Ecológicos Econômicos (CFEE). Desenhos de Uso Optimal do Território (DUOT). Ordenamento Territorial (OT). Bioenergy production, environmental safety and economic developmentem Abstract This article proposes to debate about the relationship between environmental security, conservation of biomes, production of material for bioenergy and structured management of land use in rural properties for soil and watershed conservation. For this, it is proposed to use the methodology called Ecological Economic Forest Corridors (CFEE). To guarantee its methodological success, it is necessary that they are, in fact, ideal corridors capable of rescuing forest economical ecological spaces. Thus, before its construction, it is ideal to obey the various pedological, geomorphological, agricultural and biological postulates, called ideal conditions and summarized through the Drawings of Optimal Use of the Territory (DUOT). Only after obeying these conditions, it is possible to actually build such CFEE, seeking its maximum success, which can be verified through the experimental replication proposed by Camargo (2018), confirming that the theoretical assumptions presented here show really positive results. As a conclusion, it can be said that the models discussed here integrate a vision of ecological and economic feasibility between ecological and economic forest plantations, with conservation of the biome and water circulation, with the use of intensive agriculture for the production of biomass, hardwood, fruits and drugs with a view to the eco-sustainability of watersheds. Keywords: Ecological Economic Forest Corridors (CFEE). Drawings of Optimal Territory Use (DUOT). Territorial Planning (OT).

“Friends-and-Neighbors” Mobilization: A Field Experimental Replication and Extension

Key [1949. Southern Politics in State and Nation. New York: A.A. Knopf] observed voters tend to support local candidates at higher rates, a phenomenon he termed “friends-and-neighbors” voting. In a recent study, Panagopoulos et al. [2017. Political Behavior 39(4): 865–82] deployed a nonpartisan randomized field experiment to show that voters in the September 2014 primary election for state senate in Massachusetts were mobilized on the basis of shared geography. County ties and, to a lesser extent, hometown ties between voters and candidates have the capacity to drive voters to the polls. We partnered with a national party organization to conduct a similar, partisan experiment in the November 2014 general election for the Pennsylvania state senate. We find localism cues can stimulate voting in elections, including in neighboring communities that lie beyond the towns and counties in which the target candidate resided, at least among voters favorably disposed to a candidate and even when voters reside in the home county of the opponent.