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The paper walks through three studies where text is used to quantify attitudes and actions. Ultimately, the paper argues that expression is sufficiently demanding that it should be understood as a form of action. Read the paper here: www.cambridge.org/core/journal...

The authors introduce a methodology that integrates LMs and structured coding schemes to classify open-ended survey responses cost-effectively and find that LMs can capture democratic perceptions and handle data abstractions. Read the full paper here: www.cambridge.org/core/journal...

Currently in FirstView: In “Estimating Treatment Effects on Proportions with Synthetic Controls,” @bogatyrev.bsky.social and @lstoetze.bsky.social examine synthetic control methods (SCMs) and make the case for jointly estimating synthetic controls across multiple compositional outcomes.

They validate this method using pre-election polling from the 2022 Michigan midterm and find that their calibrated MRP estimates reduce error by as much as two-thirds. You can read the full paper here: www.cambridge.org/core/journal...

Currently in FirstView: In “Using Multilingual Language Technology to Classify Open-Ended Survey Responses: Conceptions of Democracy in a Cross-Cultural Survey Setting,” S. Dahlberg, L. Dürlich, S. Axelsson, Y. Zhao, and J. Nivre examine the use of LMs in analyzing open-ended survey responses.

Using a simulation and two replication studies, they demonstrate that this approach adheres to the compositional data constraints and offers a more accurate interpretation of estimated treatment effects for proportional outcomes. You can read the paper here: www.cambridge.org/core/journal...

Currently in FirstView: In “Text as Behavior,” @owasow.bsky.social proposes using features of open-ended tasks to study text as behavior. Stats like the number of characters can approximate effort and significantly improve estimation.

Currently in FirstView: In “Democracy Manifest or Democracy Latent? A Unified Framework for Identifying Regime Types and Transitions,” @omerorsun.bsky.social and Muhammet A. Bas develop and validate a framework to study regimes that addresses measurement uncertainty and missing data.

Currently in FirstView: In “Improving Small-Area Estimates of Public Opinion by Calibrating to Known Population Quantities,” @wpmarble.bsky.social and Josh Clinton provide a framework for incorporating known population data to improve estimates of small subgroups in MRP models.

Their framework, UNITAS, reduces the dependence of inferences on specific datasets, cut-offs, magnitude-of-change and time-window assumptions, while efficiently handling missingness and measurement uncertainty. You can read the paper here: www.cambridge.org/core/journal...