I checked 7 public opinion journals on Friday, November 13, 2026 using the Crossref API. For the period November 06 to November 12, I found 8 new paper(s) in 4 journal(s).

Journal of Elections, Public Opinion and Parties

Citizen preferences for their legislative body: evidence from the United States
Neil S. Williams, Aaron A. Hitefield, Karlee L. Kimbrell
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Who counts as a niche party? Programmatic distinctiveness and local party competition
Andreas Nordang Uhre, Mikael Poul Johannesson
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Candidate positioning or limited choice? Voter–candidate fit in the 2024 US election
Samir Varma
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Journal of Official Statistics

A Robust Measure of Wealth Inequality in Small Areas
Silvia Scarpa, Stefan Sperlich
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This work provides a guide to small area estimation of the quantile ratio index (QRI) to measure wealth inequality across socio-economic or geographical groups. Wealth exhibits extreme positive skewness, rendering traditional inequality measures unreliable, as they are highly sensitive to extreme values. The QRI’s robustness to this phenomenon addresses this problem, making it particularly interesting for small area estimation problems. Relying on well-established methods, we employ a logit-transformed area-level model with bias-corrected back-transformation, validate it through model-based simulations, and apply it to Italian Survey on Household Income and Wealth data. The results confirm the expected strong heterogeneity in wealth inequality across municipalities and household groups, now based on an indicator that is much more robust than the traditional ones. This work thereby highlights the practical value of combining robust inequality measurement with small area estimation techniques for the production of reliable wealth inequality estimates in policy-relevant domains.
Testing a Targeted Refusal Conversion Strategy for an Establishment Web Survey
Ellen Laupper, Ulf-Dietrich Reips
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The paper proposes a strategy for implementing an automated and interactive feature of persuasion for late and nonrespondents in establishment web surveys. The response rate for establishment web surveys is under constant pressure, and strategies to motivate small establishments are particularly costly. Consequently, the tested survey design feature, which emulates the approach of telephone follow-ups in interviewer-administered surveys, offers a potentially new, practical, and cost-effective strategy to counteract these shortcomings. A randomly selected sample of late and nonresponding establishments ( n = 3,000) of a well-established Swiss web survey of establishments was asked to indicate their primary nonresponse reason and was then randomly assigned to one of three appeal types: (1) a targeted appeal addressing their previously indicated nonresponse reason, (2) a general appeal addressing the same reasons as the study’s standard reminder letter, or (3) a main appeal addressing the main reason for nonresponse, “lack of time.” The findings suggest that the type of appeal has no significant impact on response or data quality, whereas the reason given for nonresponse and the timing within the survey period do have an impact. The implications of these findings for survey design research are discussed.
Automatically Coding Economic Activities via Semantic Search
Francesco Ortame, Mauro Bruno
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Automated coding of free-text descriptions is a core challenge for National Statistical Institutes (NSIs), which must map colloquial respondent inputs to official taxonomies under strict reliability and transparency requirements. Rule-based systems, the current standard, demand extensive, ongoing human effort to author and maintain the large manual rule sets needed to cover the diversity of real world inputs. This paper evaluates embedding-based semantic retrieval as a lower-maintenance alternative, using the Italian ATECO classification as the empirical setting. We compare several knowledge base construction strategies across seven sentence transformer models (ranging from 118 million to 8 billion parameters), evaluated against 33,544 real-world queries labelled by CIRCE, Istat’s operational rule-based coder, at the 5-digit and 2-digit levels. Moderate-size models (300–700 million parameters) are competitive with much larger ones, achieving Hit@5 above 0.82 and 0.94 at the respective levels. Query length is a significant performance driver, and manual mismatch analysis reveals that roughly one in five disagreements with CIRCE reflect label noise rather than retrieval errors. Embedding-based retrieval thus offers competitive performance with no task-specific training data and a fraction of the human labour required by rule-based approaches.

Journal of Survey Statistics and Methodology

Bayesian Benchmarking Small Area Estimation via Entropic Tilting†
Shonosuke Sugasawa, Genya Kobayashi, Yuki Kawakubo
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The problem of benchmarking estimation and its risk evaluation is practically important in small area estimation. While Bayesian methods have been widely adopted in small area estimation, existing benchmarking approaches are often ad hoc, such as projecting each Markov chain Monte Carlo draw to satisfy the constraint. In contrast, our work provides a unified Bayesian formulation based on entropic tilting, which offers a more principled way to define the benchmarked posterior distribution. This approach yields benchmarked point estimates together with coherent uncertainty quantification. We first introduce general Monte Carlo methods for obtaining a benchmarked posterior under hierarchical Bayesian approaches and then show that the benchmarked posterior under empirical Bayesian frameworks can be obtained in an analytical form for some small-area models. We demonstrate the usefulness of the proposed method through simulation and empirical studies.

Social Science Computer Review

Fine-Tuned Large Language Models for Theory-Aligned Social Science Content Analysis: Evidence From Collaborative Learning Discourse
Yoonhee Shin, Youngkwan Son, Haengkyung Lee, Minyoung Kim
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Theory-based content analysis is central to social science research, but coding context-dependent interactional discourse remains difficult to scale. This study examines whether fine-tuned large language models (LLMs) can support theory-aligned, episode-level coding of complex social interaction, using socially shared metacognitive regulation (SSMR) in collaborative learning discourse as a demanding empirical case. Using a human-coded dataset of 2,372 discourse episodes from undergraduate collaborative problem-solving, we adapted the large language model content analysis (LACA) framework through an AI-aligned codebook, prompt design, and supervised fine-tuning. Prompt-only and fine-tuned models were compared across episode-level classification and multilayer SSMR coding. Fine-tuned models substantially improved agreement with human coding and predictive performance relative to prompt-only baselines. Performance was strongest for episode-level classification and several higher-support coding layers, whereas errors remained concentrated in sparse, boundary-prone, and highly interpretive categories requiring social-intent reconstruction and temporally unfolding context. The findings extend recent work on LLM-assisted qualitative and content analysis by showing both the promise and limits of supervised, theory-aligned automation for multilayer social interaction coding. Overall, the study supports a human-centered approach to LLM-assisted content analysis, in which automated coding can support more stable coding tasks while human expertise remains essential for ambiguous, boundary-prone, and interpretively demanding judgments.