Putting “Human Crowds” in the Loop of Bibliography Evaluation: A Collaborative Working Environment for CSCW Publications

The current impact of financial crisis on societal and scientific frameworks has raised the need to harvest and evaluate vast volumes of data in a socially-mediated interaction context to reduce knowledge gaps and accelerate innovation at a global scale. Existing mechanisms are inefficient for a single human to classify and transform data into knowledge patterns from a large number of publications. This time-consuming and computationally difficult activity requires a substantial cognitive effort grounded on scientific metrics and theoretical foundations to produce quality metadata through different knowledge representations. This paper reports on a work in progress community self-organizing bibliographic information system for semantic analytics focused on what scientific research data mean, and how they can be best interpreted through a division of intellectual labor among social, computer, and citizen scientists. Such a crowd labor ecosystem should be not restricted to traditional bibliometric approaches, attempting to uncover patterns and trends in publication data sets whilst intellectual connections can be examined to demonstrate how a scientific field is conceptually, intellectually, and socially structured.