Social Science Space

Transcribing can be a pain, and although recent progress in speech recognition software has helped, it remains a challenge. Speech recognition programs, do, however, raise ethical/consent issues: what if person-identifiable interview data is transcribed or read by someone who was not given the consent to do so? Furthermore, some conversational elements aren’t transcribed well by pattern recognition programs.

Most institutions see the market as the only legitimate form of organization, but different visions towards public policy, some involving artificial intelligence, have been the subject of consideration from academics and politicians alike. Under what circumstances, and to what extent, could artificial intelligence replace the market as the end-all guiding force in crafting reasonable public policy? Brexit may play a leading role in the transition.

Today, and into the future, consulting archival documents increasingly means reading them on a screen. This brings with it opportunity — imagine being able to search for keywords across millions of documents, leading to radically faster search times — but also challenge, as the number of electronic documents increases exponentially.

The future of the academic monograph has been questioned for over two decades. At the heart of this ‘monograph crisis’ has been a publishing industry centred on the print publication of monographs and a failure and lack of incentives to develop business models that would support a transition to open digital monographs. In this post Mike Taylor argues that if monographs are to be appropriately valued, there is a pressing need to further integrate monographs into the digital infrastructure of scholarly communication. Failing this, the difficulty in tracking the usage and discovery of monographs online, will likely make the case for justifying further investment in monographs harder.

More textual data than ever before are available to computational social scientists—be it in the form of digitized books, communication traces on social media platforms, or digital scientific articles. Researchers in academia and industry increasingly use text data to understand human behavior and to measure patterns in language.

For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. But, at least in the area of text analysis (AKA content analysis, or natural language processing), the old limits are crumbling

Creators and participants in the Evidence Synthesis Hackathon ask what’s the solution to coping with the increasing volume of evidence needed to build effective, solid policy? They argue that technology is the key. With accessible software tools and workflows, machines can be left to do the laborious work so that people can focus on planning, thinking and doing.

Burned out by the hamster-wheel of academe and the regime of metrics, John Postill decided the tonic would be to write a spoof spy thriller about a Spanish nerd with a silly name who moves to London in 1994 and accidentally foils a terrorist plot by an evil anthropologist.

Text Wash, a new software tool that anonymizes personally identifiable text data, making it accessible to social scientists without compromising its usability for research, has just won the SAGE Concept Grant. This year’s award comes to roughly $30,000.

Today, researchers are using LinkedIn data in a variety of ways: to find and recruit participants for research and experiments to analyze how the features of this network affect people’s behavior and identity or how data is used for hiring and recruiting purposes.

As SAGE Ocean builds up to this year’s winner announcement of the SAGE Concept Grant, they caught up with the three winners from 2018 to see what they’ve been up to and how the seed funding has helped in the development of their tools. In this post we chatted with MiniVan, a project of the Public Data Lab.

As SAGE Ocean builds up to this year’s winner announcement of the 2019 SAGE Concept Grant, they caught up with the three winners from 2018 to see what they’ve been up to and how the seed funding has helped in the development of their tools. Here is the second in a series of three interviews with the winners.