Understanding cross-national news coverage is central to international communication research. However, quantifying how countries appear in the news remains methodologically challenging: simple keyword counts lack context, while manual coding does not scale. This paper introduces NEClass, a reproducible, open-source pipeline that combines standard Named Entity Recognition (NER) with a fine-tuned Large Language Model (LLM) to classify entities' country affiliations. Unlike static dictionary approaches, NEClass leverages the latent world knowledge of modern LLMs and local context to resolve ambiguities dynamically. I demonstrate the pipeline's validity regarding country attribution (weighted F1 of 0.95), show that it performs more reliably than established baseline approaches (including keyword matching and zero-shot LLM prompting), and illustrate its utility through a case study of the Eurozone crisis coverage in German and Italian newspapers. Ultimately, NEClass provides researchers with a robust and accessible toolkit to operationalize international attention patterns beyond the limitations of purely keyword-based metrics.
Concerns about publication bias and the replicability of psychological research have motivated the development of methods that evaluate the credibility of published significant results. Two prominent approaches are p-curve and z-curve, which use the distribution of significant p-values to estimate the evidential value of a set of studies. Although both methods rely on selection models, they differ critically in how they handle heterogeneity in true effect sizes and statistical power. We systematically vary heterogeneity in effect sizes (tau) across a realistic range and show that both methods perform well when heterogeneity is small, but only z-curve performs well when heterogeneity is moderate. We illustrate the practical consequences using data from the Reproducibility Project, showing that only z-curve correctly predicts that many replication attempts will fail. Because z-curve performs as well as or better than p-curve across conditions, we recommend using z-curve to evaluate the credibility and evidential value of meta-analytic findings, particularly when meta-analyses synthesize conceptual rather than direct replications.
Applied data analysts who recognize the power of numbers express concern for erasing smaller demographic groups with their statistical analyses. Data analysts ought to strive for representative data categories when numbers impact participants' lives, yet underrepresented groupmembers are often lumped into nonrepresentative categories or excluded from analysis. We conducted interviews with 31 applied data analysts to thematically investigate how they prioritized group inclusion. We learned analysts employed mostly a QuantCrit approach toward social group inclusion by (a) disaggregating demographic social groups when appropriate, (b) educating leadership, (c) acknowledging demographic missingness and error, (d) providing response-fluid survey options, (e) engaging in reflexivity, and (f) codesigning demographic measures. They also employed qualitative methods to present marginalized groups' stories and counterstories. Contributions are five-fold: First, the results provide evidence that qualitative data representing smaller demographic groups were perceived as more effective in attracting funding and influencing change than numerical data. Second, inclusion conceptual boundaries are explored by studying professionals who confront normative quantitative data practices. Third, applied professionals treated demographic measurement structure with flexibility to create culturally and locally relevant demographic measures. Fourth, we share the research utility of QuantCrit tenets. Last, and most importantly, reflexivity is required at every data stage.
