Archive for the ‘Social Sciences’ Category

Cool GSS training video! And cumulative file 1972-2012!

Sunday, March 10th, 2013

Cool GSS training video! And cumulative file 1972-2012! by Andrew Gelman.

From the post:

Felipe Osorio made the above video to help people use the General Social Survey and R to answer research questions in social science. Go for it!

From the GSS: General Social Survey website:

The General Social Survey (GSS) conducts basic scientific research on the structure and development of American society with a data-collection program designed to both monitor societal change within the United States and to compare the United States to other nations.

The GSS contains a standard ‘core’ of demographic, behavioral, and attitudinal questions, plus topics of special interest. Many of the core questions have remained unchanged since 1972 to facilitate time-trend studies as well as replication of earlier findings. The GSS takes the pulse of America, and is a unique and valuable resource. It has tracked the opinions of Americans over the last four decades.

The information “gap” is becoming more of a matter of skill than access to underlying data.

How would you match the GSS data up to other data sets?

Computational Folkloristics

Friday, January 18th, 2013

JAF Special Issue 2014 : Computational Folkloristics – Special Issue of the Journal of American Folklore

I wasn’t able to confirm this call at the Journal of American Folklore, but wanted to pass it along anyway.

There are few areas with the potential for semantic mappings as rich as folklore. A natural for topic maps.

From the call I cite above:

Submission Deadline Jun 15, 2013
Notification Due Aug 1, 2013
Final Version Due Oct 1, 2013

Over the course of the past decade, a revolution has occurred in the materials available for the study of folklore. The scope of digital archives of traditional expressive forms has exploded, and the magnitude of machine-readable materials available for consideration has increased by many orders of magnitude. Many national archives have made significant efforts to make their archival resources machine-readable, while other smaller initiatives have focused on the digitization of archival resources related to smaller regions, a single collector, or a single genre. Simultaneously, the explosive growth in social media, web logs (blogs), and other Internet resources have made previously hard to access forms of traditional expressive culture accessible at a scale so large that it is hard to fathom. These developments, coupled to the development of algorithmic approaches to the analysis of large, unstructured data and new methods for the visualization of the relationships discovered by these algorithmic approaches – from mapping to 3-D embedding, from time-lines to navigable visualizations – offer folklorists new opportunities for the analysis of traditional expressive forms. We label approaches to the study of folklore that leverage the power of these algorithmic approaches “Computational Folkloristics” (Abello, Broadwell, Tangherlini 2012).

The Journal of American Folklore invites papers for consideration for inclusion in a special issue of the journal edited by Timothy Tangherlini that focuses on “Computational Folkloristics.” The goal of the special issue is to reveal how computational methods can augment the study of folklore, and propose methods that can extend the traditional reach of the discipline. To avoid confusion, we term those approaches “computational” that make use of algorithmic methods to assist in the interpretation of relationships or structures in the underlying data. Consequently, “Computational Folkloristics” is distinct from Digital Folklore in the application of computation to a digital representation of a corpus.

We are particularly interested in papers that focus on: the automatic discovery of narrative structure; challenges in Natural Language Processing (NLP) related to unlabeled, multilingual data including named entity detection and resolution; topic modeling and other methods that explore latent semantic aspects of a folklore corpus; the alignment of folklore data with external historical datasets such as census records; GIS applications and methods; network analysis methods for the study of, among other things, propagation, community detection and influence; rapid classification of unlabeled folklore data; search and discovery on and across folklore corpora; modeling of folklore processes; automatic labeling of performance phenomena in visual data; automatic classification of audio performances. Other novel approaches to the study of folklore that make use of algorithmic approaches will also be considered.

A significant challenge of this special issue is to address these issues in a manner that is directly relevant to the community of folklorists (as opposed to computer scientists). Articles should be written in such a way that the argument and methods are accessible and understandable for an audience expert in folklore but not expert in computer science or applied mathematics. To that end, we encourage team submissions that bridge the gap between these disciplines. If you are in doubt about whether your approach or your target domain is appropriate for consideration in this special issue, please email the issue editor, Timothy Tangherlini at tango@humnet.ucla.edu, using the subject line “Computational Folkloristics query”. Deadline for all queries is April 1, 2013.

Timothy Tangherlini homepage.

Something to look forward to!

One Culture. Computationally Intensive Research in the Humanities and Social Sciences…

Monday, July 2nd, 2012

One Culture. Computationally Intensive Research in the Humanities and Social Sciences, A Report on the Experiences of First Respondents to the Digging Into Data Challenge by Christa Williford and Charles Henry. Research Design by Amy Friedlander.

From the webpage:

This report culminates two years of work by CLIR staff involving extensive interviews and site visits with scholars engaged in international research collaborations involving computational analysis of large data corpora. These scholars were the first recipients of grants through the Digging into Data program, led by the NEH, who partnered with JISC in the UK, SSHRC in Canada, and the NSF to fund the first eight initiatives. The report introduces the eight projects and discusses the importance of these cases as models for the future of research in the academy. Additional information about the projects is provided in the individual case studies below (this additional material is not included in the print or PDF versions of the published report).

Main Report Online

or

PDF file.

Case Studies:

Humanists played an important role the development of digital computers. That role has diminished over time to the disadvantage of both humanists and computer scientists. Perhaps efforts such as this one will rekindle what was once a rich relationship.