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研究方法

Running With Scissors? Integrating GPT Models Into Public Policy Research

·627 字·2 分钟
Polihot 公共政策 研究方法 LLM相关 Large Language Models GPT Public Policy Text Annotation Advocacy Coalition Framework
PSJ — 2026-06-23 Original Running With Scissors? Integrating GPT Models Into Public Policy Research Giulia Mariani, Allegra H. Fullerton Abstract ABSTRACT The integration of large language models (LLMs) into public policy research presents both exciting opportunities and methodological challenges. This research note explores how OpenAI's GPT can be used to semi‐automate the annotation of legislative testimony within the Advocacy Coalition Framework, focusing on emotion‐belief dyads. Building on Emotion‐Belief Analysis, we demonstrate how GPT can assist in identifying these complex constructs under human supervision. Our contributions are threefold: (1) we provide practical guidance for applying LLMs to publicly available textual data, (2) we propose a semiautomated workflow that strengthens conceptual clarity, transparency, consistency, replicability, and accessibility, and (3) we reflect on the ethical and methodological implications of LLM‐assisted research. As LLMs continue to advance, this research note aims to help scholars balance innovation with rigor and integrate these tools responsibly into policy research, offering lessons that extend to the study of frames, discourses, narratives, and other ideational dimensions of policymaking.

Mapping the temporal evolution of causal effects in public administration and policy research

·683 字·2 分钟
Polihot 公共行政 研究方法 Causal Inference Bayesian Changepoint Models Temporal Dynamics Public Administration Policy Evaluation
JPART — 2026-06-15 Original Mapping the temporal evolution of causal effects in public administration and policy research Lefteris Jason Anastasopoulos, Inkyu Kang Abstract Abstract Recent growth in the use of randomized and quasi-experiments in public administration and policy research has advanced the ability to establish cause-and-effect relationships. However, many studies adopt static conceptions of causality, focusing on snapshots or time-averaged effects while overlooking how the effects change over time. This oversight is problematic, as interventions of scholarly interest, such as leadership training or the adoption of new technologies, are likely to produce impacts that unfold in various ways. In this paper, we propose a conceptual framework for understanding the temporal dynamics of causal effects and their implications for research hypotheses and design. We then introduce Bayesian changepoint models (BCMs) as a methodological tool for detecting shifts in the average, variance, or trend of causal effect series, providing a more rigorous yet accessible alternative to visually inspecting graphs. Next, we demonstrate the application of BCMs with an illustrative example derived from simulation data as well as a real-world case examining the effect of body-worn cameras on police officers’ use of force. Finally, we discuss how examining temporal changes in effects can advance theoretical understanding of why and how they occur, as well as inform the design and implementation of policies and strategies in practice.

The Effects of Religious Messages and Endorsements on Political Attitudes: A Meta-Reanalysis

·525 字·2 分钟
Polihot 政治行为 研究方法 Religion Political Attitudes Meta-Analysis Experiments Endorsements
APSR — pp. 1-22 2026-06-10 Original The Effects of Religious Messages and Endorsements on Political Attitudes: A Meta-Reanalysis RADHA SARKAR, ALEXANDER COPPOCK Abstract The experimental study of the effects of religious messages and endorsements by religious leaders on political attitudes has a relatively short history yet has coalesced around three main claims: religious treatments are especially effective because of their religious character, effects are larger for religious affiliates than nonaffiliates, and effects are larger for high religiosity types than low religiosity types. Here, we meta-reanalyze the experimental record (58 treatment-outcome pairs drawn from 43 studies reported in 26 papers) to probe the generalizability of these claims. Our findings indicate that these three headline claims do not generalize straightforwardly across contexts: effects are large in some cases and close to zero in others, and we find no evidence in favor of the claimed heterogeneities by religious affiliation or religiosity. Based on a census of the estimands in this literature, we offer suggestions for future research that would enhance commensurability and synthesis.

Post‐instrument bias

·547 字·2 分钟
Polihot 研究方法 Instrumental Variables Causal Inference Exclusion Restriction Sensitivity Analysis Post-Instrument Bias
AJPS — 2026-05-19 Original Post‐instrument bias Julian Schuessler, Adam N. Glynn, Miguel R. Rueda Abstract Abstract When using instrumental variables, researchers often assume that causal effects are only identified conditional on covariates. We show that the role of these covariates is often unclear and that there exists confusion regarding their ability to mitigate violations of the exclusion restriction. We explain when and how existing adjustment strategies may lead to “post‐instrument” bias. We then discuss assumptions that are sufficient to identify various treatment effects when adjustment for post‐instrument variables is required. In general, these assumptions are highly restrictive, albeit they sometimes are testable. We also show that other existing tests are possibly misleading. Then, we introduce a sensitivity analysis that uses information on variables influenced by the instrument to gauge the effect of potential violations of the exclusion restriction. We illustrate it using a published study and summarize our results in easy‐to‐understand guidelines.

Promoting Inclusive Workplaces: Conceptualizing and Measuring Inclusive Leadership in Public Organizations

·637 字·2 分钟
Polihot 公共行政 研究方法 Inclusive Leadership Public Organizations Scale Development Diversity Inclusiveness
PAR — 2026-04-28 Original Promoting Inclusive Workplaces: Conceptualizing and Measuring Inclusive Leadership in Public Organizations Tanachia S. Ashikali, Mads Pieter Van Luttervelt Abstract ABSTRACT Inclusive leadership has become a central theme in public management research to foster equity and workplace inclusiveness. While prior studies demonstrate its potential to enhance inclusiveness, psychological safety, and performance, existing conceptualizations and measures remain fragmented, emphasizing leader attitudes rather than observable behaviors. This article addresses these gaps by advancing a behavior‐focused conceptualization of inclusive leadership, grounded in an inclusivity perspective and an integration‐and‐learning perspective. Inclusive leadership is defined as leader behaviors that seek to enable employees' full participation in work processes by supporting employees in balancing needs for uniqueness and belongingness and by stimulating the exchange, discussion, and integration of diverse perspectives. Building on this, a two‐dimensional measurement is developed and validated through cognitive interviews and surveys among Dutch public employees ( N = 304) and Danish upper secondary school teachers ( N = 1275). The scale enables systematic assessment of inclusive leadership and its outcomes across contexts and diversity types.