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Past research indicates that Sociology is a low-consensus discipline, where different schools of thought have distinct expectations about suitable scientific practices. This division of Sociology into different subfields is to a large extent related to methodology and choices between qualitative or quantitative research methods. Relying on theoretical constructs of the academic prestige economy, boundary demarcation and taste for research, we examine the methodological divide in generalist Sociology journals. Using automated text analysis for 8737 abstracts of articles published between 1995 and 2017, we discover evidence of this divide, but also of an entanglement between methodological choices and different research topics. Moreover, our results suggest a marginally increasing time trend for the publication of quantitative research in generalist journals. We discuss how this consolidation of methodological practices could enforce the entrenchment of different schools of thought, which ultimately reduces the potential for innovative and effective sociological research.

African-Americans are still experiencing racial discrimination rooted in structural bias in US American society. Research has shown that this behaviour can be reduced if individuals are made conscious of their bias, but little is known about these mechanisms on a societal level. Envisaging the white-supremacist Charlottesville rally in 2017 as an event that rendered American society conscious of its racism, we scrutinise whether racial bias in the digital media has changed, comparing levels of pre- and post-Charlottesville bias. We fit word embedding models to a broad sample of largely US media and quantify bias by calculating cosine similarities between terms for black or white actors and positive or negative character traits. We find no differences in positive character traits after Charlottesville. However, African-Americans are associated substantially less with negative character traits post-Charlottesville, while white actors are semantically closer to negative traits.

Legislative committees usually combine legislative and oversight functions. Most research has focused on the former. The British House of Commons offers an opportunity to shed light on MPs' motivations to engage in the neglected oversight aspects of committee work, because its committee system separates lawmaking from investigatory functions. In this article, we examine the politics of assignment to the investigatory select committees. Using an original data set for the 2005–10 and 2010–15 Parliaments, we find that MPs' long‐standing policy interests are a strong and consistent predictor for select‐committee assignments, especially when committees' desirability increases. Our results are largely incompatible with partisan models predicting party loyalty to be a decisive factor in select‐committee assignments. Evidence suggests that the "Wright reforms" have consolidated the self‐selection mechanism operating in committee assignments. Against expectations of partisan motivations driving committee assignments, our evidence suggests that models based on informational concerns offer the most convincing rationale.

AbstractLegislators are political actors whose main goal is to get re‐elected. They use their legislative repertoire to help them cater to the interests of their principals. It is argued in this article that we need to move beyond treating electoral systems as monolithic entities, as if all legislators operating under the same set of macro‐rules shared the same set of incentives. Rather, we need to account for within‐system variation – namely, candidate selection rules and individual electoral vulnerability. Using a most different systems design, Germany, Ireland and Portugal are leveraged with both cross‐system and within‐system variation. An original dataset of 345,000 parliamentary questions is used. Findings show that candidate selection rules blur canonical electoral system boundaries. Electoral vulnerability has a similar effect in closed‐list and mixed systems, but not in preferential voting settings.

Do elected representatives have a time-constant representation focus or do they adapt their focus depending on election proximity? In this article, we examine these overlooked theoretical and empirical puzzles by looking at how reelection-seeking actors adapt their legislative behavior according to the electoral cycle. In parliamentary democracies, representatives need to serve two competing principals: their party and their district. Our analysis hinges on how representatives make a strategic use of parliamentary written questions in a highly party-constrained institutional context to heighten their reselection and reelection prospects. Using an original data set of over 32,000 parliamentary questions tabled by Portuguese representatives from 2005 to 2015, we examine how time interacts with two key explanatory elements: electoral vulnerability and party size. Results show that representation focus is not static over time and, in addition, that electoral vulnerability and party size shape strategic use of parliamentary questions.

Image recognition systems offer the promise to learn from images at scale without requiring expert knowledge. However, past research suggests that machine learning systems often produce biased output. In this article, we evaluate potential gender biases of commercial image recognition platforms using photographs of U.S. members of Congress and a large number of Twitter images posted by these politicians. Our crowdsourced validation shows that commercial image recognition systems can produce labels that are correct and biased at the same time as they selectively report a subset of many possible true labels. We find that images of women received three times more annotations related to physical appearance. Moreover, women in images are recognized at substantially lower rates in comparison with men. We discuss how encoded biases such as these affect the visibility of women, reinforce harmful gender stereotypes, and limit the validity of the insights that can be gathered from such data.