SHAOZENG ZHANG

Contributed Articles

Weighing Decisions in Monitoring and Evaluation of Clean Cookstoves

JENNIFER VENTRELLA The New School ERIN PEIFFER Oregon State University SHAOZENG ZHANG Oregon State University NORDICA MACCARTY Oregon State University This case study examines agency within monitoring and evaluation (M&E) schemes for international development projects. Specifically, it evaluates a sensor to measure fuel consumption of clean cookstoves as a method of maintaining accountability and soliciting data on stove performance. Despite trends of increasingly automated M&E, the decisions of choosing, analyzing, and translating outcomes and indicators are influenced by stakeholder input. Through various rapid ethnographic methods including surveys and interviews with government agencies, non-profits, and clean stove users, in addition to participant observation and focal follow of stove users in Central America and Uganda, the interactions and inputs of various agents throughout the project lifetime are assessed. Further, it is discussed that while not all actors were equitably engaged throughout the entirety...

Towards an Archaeological-Ethnographic Approach to Big Data: Rethinking Data Veracity

SHAOZENG ZHANG Program of Applied Anthropology, Oregon State University BO ZHAO Program of Geography, Oregon State University JENNIFER VENTRELLA Program of Mechanical Engineering and Program of Applied Anthropology, Oregon State University For its volume, velocity, and variety (the 3 Vs), big data has been ever more widely used for decision-making and knowledge discovery in various sectors of contemporary society. Since recently, a major challenge increasingly recognized in big data processing is the issue of data quality, or the veracity (4th V) of big data. Without addressing this critical issue, big data-driven knowledge discoveries and decision-making can be very questionable. In this paper, we propose an innovative methodological approach, an archaeological-ethnographic approach that aims to address the challenge of big data veracity and to enhance big data interpretation. We draw upon our three recent case studies of fake or noise data in different data environments. We approach big data as but another kind of human...