Social Science Computer Review

Fischer, Michael D.

doi: 10.1177/0894439305282575pmid: N/A

The authors present examples of how anthropologists are presently using computers to advance ethnographic research in new directions while building on what has come before. All the methods, protocols, and tools created by the authors are free, open source, and available on the Internet. The contributions are the authors’ attempts to address greater complexity through greater control over the data and structures within which anthropologists work. These methods are suitable to a large number of problems, basic and applied, across the range of anthropology from its humanities axis to its science axis. Anthropology is what anthropologists make of it, and each author is attempting to make a little bit more of anthropology and to configure anthropology for addressing old problems in new ways and positioning anthropology to address new problems and new opportunities to influence others through anthropology.

Kuznar, Lawrence A.

doi: 10.1177/0894439305282430pmid: N/A

Approaches to modeling social phenomena vary on a continuum from simple models, in which causality is clear and parameters few, to realistic, high-fidelity models designed to capture the most detailed system behavior possible in a specific setting. Anthropologists have produced both simple and high-fidelity models. The focus of this article is on high-fidelity modeling in anthropology and the special challenge its complexity presents for model comparison. Useful model comparison requires docking, or rendering models comparable, and the author presents a framework for docking based on the work of Axelrod, and Cioffi-Revilla and Gotts. Docking not only renders models more comparable, allowing for more traditional theory testing, but it also sharpens the discussion about the ontology of anthropological phenomena and how they are best represented as theories and models.

Lyon, Stephen M.; Magliveras, Simeon S.

doi: 10.1177/0894439305281494pmid: N/A

This article proposes two important points about genealogical software: (a) Not all such software need necessarily be complicated or address high level theoretical issues, and (b) diversity of data, processing, and infrastructure means that it is particularly desirable that scholars begin to understand software tools as utilities that should have flexibility, including platform independence built into the design from the outset. Following a discussion of high performance packages used by White and Houseman to analyze social networks from marital data, the authors present examples from their research that suggest that even apparently trivial, nonanalytic tasks that form part of the process of preparing data for higher end analyses may yield exciting and productive results. The authors conclude with a statement on the nature of e-science in anthropology and the implications for the types of software that will be most useful.

Read, Dwight W.

doi: 10.1177/0894439305282372pmid: N/A

The computer program Kinship Algebra Expert System (KAES) provides a graphically based framework for constructing, if possible, a generative algebraic model for the structure of a kinship terminology (the terms used to refer to one’s kin). The algebraic modeling is based on a theory of kinship terminologies elaborated through writing the software program. The theory relates the properties and structure of kinship terminologies to an underlying logic that the KAES program helps uncover and model as a generative structure. The program then relates the structural logic of a kinship terminology modeled by the KAES program to a genealogical space based on genealogical tracing of kin relations. The KAES program demonstrates the surprisingly logical character of kinship terminologies and challenges the received view of the primacy of genealogical relations in defining cultural kinship through showing how genealogical definitions of kin terms can be accurately predicted in the terminologies considered to date.

Weinstein, Matthew

doi: 10.1177/0894439305281496pmid: N/A

This article provides an overview of an open source qualitative coding and analysis program called TAMS Analyzer, where TAMS stands for Text Analysis Mark-up System. The article reviews the history and design of this software. This history focuses on transformations in the software that have allowed it to work with larger scale projects, more abstract analytic categories, and wider varieties of media. In examining the software design, the article reflects variously on the value of software-assisted qualitative research, issues of openness with respect to software standards and licensing, and transparency to the user. It concludes by looking at some future directions for software-assisted qualitative research and by noting contradictions in the qualitative marketplace that will likely shape what will be available to qualitative researchers.

Bharwani, Sukaina

doi: 10.1177/0894439305282346pmid: N/A

Understanding ethnographic data in a formal way is imperative when faced with multiple responses of humans within their environments. Knowledge Elicitation Tools (KnETs) incorporate techniques for modeling knowledge using methods long used in anthropological fieldwork and formalizing knowledge using knowledge engineering methods from computer science. KnETs enhance our understanding of our data to reveal new avenues for enquiry. KnETs support traditional participatory fieldwork methods and produce input for agent-based models, supporting a formalized link between qualitative and quantitative representations of knowledge and their interaction. The fusion of these techniques has resulted in a four-stage process that incorporates consistent verification and validation on data as it is collected by domain experts and informants. The application of this innovative methodology is successful precisely due to the mutual benefits that each technique provides by addressing current bottlenecks in both processes of ethnographic data collection and knowledge engineering.

Chen, Hsiang

doi: 10.1177/0894439305281844pmid: N/A

The implementation and the assessment of the digital experience sampling method are reported in this article. The experience sampling method is transformed into a digital format and is examined in the Web environment to elicit online Web users’ experiences through multiple iterations of an online questionnaire. By sampling Web users’ online experiences and detecting Web users’ situated experiences from a time point very close to their actual experiences, this tool may collect reliable and valid data with minimal distortion. This tool may effectively and unambiguously tap Web users’ internal experiences associated with their use of the Web. Two empirical studies are reported to verify the usefulness of this method.

Lagazio, Monica; Marwala, Tshilidzi

doi: 10.1177/0894439305281512pmid: N/A

This article develops and compares two Bayesian neural network models, a more restrictive Bayesian framework using Gaussian approximation and a less restrictive one using a hybrid version of Markov Chain Monte Carlo method (HMC), for the prediction of militarized interstate disputes (MIDs). In addition, to compare and analyze different Bayesian models for international conflict, the authors introduce a new measurement to interpret the relative influence of the model variables on the MIDs. The results indicate that the Gaussian approximation and HMC models are not statistically different in their performance. However HMC correctly recognized a marginally higher number of militarized disputes whose classification is important for policy purpose. On the variable effect, both models indicate similar patter of influences, where the two key liberal variables, democracy and economic interdependence, produce a strong dynamic feedback loop among each other, which greatly increases or decreases the probability of MIDs.

Grafton, Carl

doi: 10.1177/0894439305281497pmid: N/A