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.
Promotional banner for Political Analysis announcing 'New Issue Online' on a yellow and red background.
NEW ISSUE from @polanalysis.bsky.social -
Political Analysis - Volume 34 - Issue 1 - January 2026 - https://cup.org/4aAPBWB
Their method is demonstrated using social media conversations surrounding the MeToo movement and the 2020 presidential election. This method is an alternative to off-the-shelf methods such as LDA, which are computationally inefficient. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Analyzing Political Text at Scale with Online Tensor LDA,” @sarakangaslahti.bsky.social, Danny Ebanks, @jeankossaifi.bsky.social, Anqi Liu, @rmichaelalvarez.bsky.social, and Anima Anandkumar introduce a topic modeling method that scales linearly to billions of documents.
They focus on two key political traits, agency and communion, and extract these traits from a large corpus of politicians’ speeches. This approach is validated using human-labeled data and functional tests. You can read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Measuring Politicians’ Public Personality Traits Using Computational Text Analysis: A Multimethod Feasibility Study for Agency and Communion,” @lukasbirkenmai1.bsky.social and Clemens Lechner introduce an approach to infer politicians’ personality traits from text data.
They use an image classification task to compare assessments of GenAI models to a national and locally representative survey sample. Overall, GenAI is biased toward national averages over local perspectives. You can read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood Perception,” Paige Bollen, @joehigton.bsky.social, and @msands.bsky.social test which populations Generative AI is most representative of.
They find that survey professionalism is common, but there is limited evidence that survey professionals lower data quality. Professionals do not systematically differ from non-professionals and don’t exhibit more response instability. Read the paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Survey Professionalism: New Evidence from Web Browsing Data,” Bernhard Clemm von Hohenberg, @tiagoventura.bsky.social, Tiago Ventura, @jonathannagler.bsky.social, @ericka.bric.digital, & Magdalena Wojcieszak provide evidence on survey professionalism across three samples.
The plot staircase is introduced as a way of identifying the relative importance of a graph characteristic compared to a baseline. This method is demonstrated using data on economic growth, job creation, and the COVID-19 vaccine rollout. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical Preferences,” @talbotmandrews.bsky.social, Justin Curl, and Markus Prior examine how visual characteristics influence preferences. They find that people prefer increasing trends.
The authors provide a framework to evaluate codebook-LLM measurement, classifying unlabeled documents with LLMs given a human-written codebook. Ultimately, supervised instruction-tuning can substantially improve performance. Read the full paper here: www.cambridge.org/core/journal...
Currently in FirstView: In “Codebook LLMs: Evaluating LLMs as Measurement Tools for Political Science Concepts,” @ahalterman.bsky.social and @katakeith.bsky.social show how “off-the-shelf” LLMs have limitations in faithfully following real-world codebook operationalizations.
