A selected glossary of electronic data interchange and related terms

School of Managemen

Homo Datumicus : correcting the market for identity data

Effective digital identity systems offer great economic and civic potential. However, unlocking this potential requires dealing with social, behavioural, and structural challenges to efficient market formation. We propose that a marketplace for identity data can be more efficiently formed with an infrastructure that provides a more adequate representation of individuals online. This paper therefore introduces the ontological concept of Homo Datumicus: individuals as data subjects transformed by HAT Microservers, with the axiomatic computational capabilities to transact with their own data at scale. Adoption of this paradigm would lower the social risks of identity orientation, enable privacy preserving transactions by default and mitigate the risks of power imbalances in digital identity systems and markets

Special Libraries, December 1966

Volume 57, Issue 10https://scholarworks.sjsu.edu/sla_sl_1966/1009/thumbnail.jp

Special Libraries, May-June 1974

Volume 65, Issue 5-6https://scholarworks.sjsu.edu/sla_sl_1974/1004/thumbnail.jp

A review of name-based ethnicity classification methods and their potential in population studies

Several approaches have been proposed to classify populations into ethnic groups using people's names, as an alternative to ethnicity self-identification information when this is not available. These methodologies have been developed, primarily in the public health and population genetics literature in different countries, in isolation from and with little participation from demographers or social scientists. The objective of this paper is to bring together these isolated efforts and provide a coherent comparison, a common methodology and terminology in order to foster new research and applications in this promising and multidisciplinary field. A systematic review has been conducted of the most representative studies that develop new name-based ethnicity classifications, extracting methodological commonalities, achievements and shortcomings; 13 studies met the inclusion criteria and all followed a very similar methodology to create a name reference list with which to classify populations into a few most common ethnic groups. The different classifications' sensitivity varies between 0.67 and 0.95, their specificity between 0.80 and 1, their positive predicted value between 0.70 and 0.96, and their negative predicted value between 0.96 and 1. Name-based ethnicity classification systems have a great potential to overcome data scarcity issues in a wide variety of key topics in population studies, as is proved by the 13 papers analysed. Their current limitations are mainly due to a restricted number of names and a partial spatio-temporal coverage of the reference population data-sets used to produce name reference lists. Improved classifications with extensive population coverage and higher classification accuracy levels will be achieved by using population registers with wider spatio-temporal coverage. Furthermore, there is a requirement for such new classifications to include all of the potential ethnic groups present in a society, and not just one or a few of them. Copyright (c) 2007 John Wiley & Sons, Ltd

Event-based media monitoring methodology for Human Rights Watch

Executive Summary This report, prepared by a team of researchers from the University of Minnesota for Human Rights Watch (HRW), investigates the use of event-based media monitoring (EMM) to review its application, identify its strengths and weaknesses, and offer suggestions on how HRW can better utilize EMM in its own work. Media monitoring systems include both human-operated (manual) and automated systems, both of which we review throughout the report. The process begins with the selection of news sources, proceeds to the development of a coding manual (for manual searches) or “dictionary” (for automated searches), continues with gathering data, and concludes with the coding of news stories. EMM enables the near real-time tracking of events reported by the media, allowing researchers to get a sense of the scope of and trends in an event, but there are limits to what EMM can accomplish on its own. The media will only cover a portion of a given event, so information will always be missing from EMM data. EMM also introduces research biases of various kinds; mitigating these biases requires careful selection of media sources and clearly defined coding manuals or dictionaries. In manual EMM, coding the gathered data requires human researchers to apply codebook rules in order to collect consistent data from each story they read. In automated EMM, computers apply the dictionary directly to the news stories, automatically picking up the desired information. There are trade-offs in each system. Automated EMM can code stories far more quickly, but the software may incorrectly code stories, requiring manual corrections. Conversely, manual EMM allows for a more nuanced analysis, but the investment of time and effort may diminish the tool’s utility. We believe that both manual and automated EMM, when deployed correctly, can effectively support human rights research and advocacy