All models consistently attributed more liberal ideologies to women while racial associations differed by model. They conclude that using these models political content analysis may unknowingly introduce model-specific confounds. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “From Faces to Politics: Vision-Language Models (Sometimes) Link Visual Demographic Characteristics to Ideological Labels,” S. Jeon, M. Lee, @jacobmontgomery.bsky.social, and @calvinklai.bsky.social ask how models use demographics as shortcuts for ideological attribution.
They propose an assumption to account for nonignorable missingness in the outcome. Integrating this assumption with covariate information provides an identifiable method for estimating voter turnout. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Correcting Nonignorable Nonresponse Bias in Turnout Estimation Using Callback Data,” Xinyu Li, Naiwen Ying, Kendrick Qijun Li, Xu Shi, and Wang Miao look at the role of callback data as a way of adjusting for nonresponse bias in estimating voter turnout.
The authors offer a GP framework and highlight certain use cases. GPs have the ability to incorporate extrapolation uncertainty, widening intervals as predictions rely more heavily on assumptions beyond the observed support. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Inference at the Data’s Edge: Gaussian Processes for Estimation and Inference in the Face of Extrapolation Uncertainty,” Soonhong Cho, Doeun Kim, and Chad Hazlet illustrate the value of Gaussian Processes (GPs) for capturing counterfactual uncertainty.

The model is validated with data on coalition government survival, showing that ignoring party-level dependencies can produce misleading conclusions at all levels of analysis. The paper also introduces an accompanying R package. You can read it here: www.cambridge.org/core/journal...
Currently in FirstView: In “A Multilevel Model for Coalition Governments: Uncovering Party-Level Dependencies Within and Between Governments,” Benjamin Rosche extends the Multiple Membership Multilevel Model to represent the multilevel structure of coalition government data.
The adjusted estimates show that congressional polarization and its increase over time are ever greater than previously thought, and the electoral penalty associated with ideological extremism is greater than previously thought. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Accounting for Protest Voting in the U.S. Congress,” Anthony Fowler and Jeffrey B. Lewis estimate a model of congressional voting that allows for non-ideological protest voting. This has significant implications for roll-call estimates of ideology.
The model offers insights about stability, the direction of causation between attitudes, and their relative influence. They use their model to show the role of ideology on attitudes toward government spending and immigration. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “A Dynamic Discrete Choice Approach to Attitude Stability and Constraint,” @alecia-nepaul.bsky.social and Steven Stern introduce a discrete choice framework to identify influential attitudes within attitude systems.
The findings challenge Gamson’s law: the idea that cabinet ministries in multiparty democracies are distributed in proportion to seats. Because portfolio and seats are mutually dependent, ILR addresses concerns of bias and uncertainty. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: in “Refining Gamson: The Isometric Log-Ratio Transformation and Portfolio Proportionality in Multiparty Governments,” Lanny Martin and Georg Vanberg propose the isometric log-ratio (ILR) as an alternative to the additive log-ratio (ALR) transformation.
They demonstrate the utility of their models by analyzing civil rights protests in the US. These models are useful because many datasets in political science are nested and can potentially have diffusion processes at multiple levels. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: in “Modeling Hierarchical Spatial Interdependence for Limited Dependent Variables,” Ali Kagalwala and Kankyeul Yang propose a class of spatial hierarchical models with binary outcomes to account for spatially independent and spatially dependent unobserved group effects.
Using a dataset of all televised U.S. presidential debates from 1960 to 2020, the authors highlight many applications including forced alignment of audio text, speech characterization, and custom classification models. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: in “Potential and Pitfalls of Audio as Data for Political Research: Alignment, Features, and Classification Models,” @r-mestre.bsky.social and Matt Ryan provide solutions to challenges encountered when analyzing audio data in political science.
In their replication, they show that the association between education and acquiescence is an artifact of low-quality survey responses. Scholars should be cautious about over-interpreting conditional effects in low-quality survey panels. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Survey Quality and Acquiescence Bias: A Cautionary Tale,” Andrés Cruz, Adam Bouyamourn, and @joeornstein.bsky.social discuss the dangers of drawing inferences from low-quality survey datasets. They replicate an experiment on acquiescence and misinformation.
ConfliBERT is open source and is easily deployed and replicable. It is significantly better on comparable, relevant quality metrics and faster than other LLMS that use decoder technologies with graphical processing units (GPUs). Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Extractive versus Generative Language Models for Political Conflict Text Classification,” P. Brandt, S. Alsarra, F. D’Orazio, @dagmarheintze.bsky.social, L. Khan, S. Meher, @javierosorio.bsky.social, & M. Sianan review and benchmark the ConfliBERT model.
The January 2026 issue of Political Analysis is out and currently free to read. Check it out now through the end of February!
Their BSA method is designed to address concerns about confounders that cannot be addressed by fixed effects. They illustrate this using a Monte Carlo simulation study and an empirical example on the effect of war on tax rates. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Bayesian Sensitivity Analysis for Unmeasured Confounding in Causal Panel Data Models,” Licheng Liu and Teppei Yamamoto develop a Bayesian sensitivity analysis (BSA) method for causal panel data analysis.
They find that complex prompting strategies can lead to improved model performance. The authors also offer several recommendations for researchers using LLMs for stance detection in political texts. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Model,” Max Griswold, Michael Robbins, and @sociologian.bsky.social evaluate fine-tuning strategies to improve LLM performance using social media data surrounding the 2020 election.
The Political Domain Enhanced BERT-based Algorithm for Textual Entailment (DEBATE) is benchmarked against other popular supervised classifiers. Ultimately, DEBATE is both efficient and completely open source. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Political DEBATE: Efficient Zero-Shot and Few-Shot Classifiers for Political Text,” Michael Burnham, Kayla Kahn, Ryan Yang Wang, and Rachel Peng introduce DEBATE, a new open source foundation model for classifying political documents.
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NEW ISSUE from @polanalysis.bsky.social -
Political Analysis - Volume 34 - Issue 1 - January 2026 - https://cup.org/4aAPBWB