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Preface

The notion that one can begin anything at all from scratch, free from the past, or unindebted to others, could not conceivably be more wrong (Magee 1974, 69).

This volume is written in Markdown, and includes all analysis code employed in the study, providing a means for others to reproduce (exactly) those results discussed and expounded upon in the manuscript. The reproducible nature of this undertaking provides a means for others to critically assess and evaluate the various analytical components of this study (Gray and Marwick 2019; Peng 2011; Gandrud 2014), which is a necessary requirement for the production of reliable knowledge.

How do we know what we know? Upon whose work will this new project build? How have others addressed a particular problem in their research? In practice, the production of reliable knowledge is among the paramount goals of archaeology. Such knowledge provides the requisite foundation needed to advance our efforts to interpret and improve upon our shared understanding of the past. Recent methodological advances in bibliometrics provide a useful toolkit for approaching these important questions systematically, and visualizing the results. What follows is an example of a bibliometric study of indexed publications related to geometric morphometrics in archaeology.

0.1 Acknowledgments

We extend our gratitude to the MORPHMET and Morph2016-2019 communities, as well as our colleagues at archaeologicalmorphometrics.com for their comments on earlier versions of the networks.

This volume enlists a variety of tools from the Open Review Toolkit, as well as the code provided for the bookdown package, and follows the excellent example advanced by Ben Marwick. I extend my gratitude to all who contribute comments and constructive criticisms throughout the development and maturation of this project. This document will remain in open review until the manuscript is published.

0.2 Funding

Funding for this project was provided to RZS by the United States Forest Service, National Forests and Grasslands in Texas (15-PA-11081300-033 and 20-PA-11081300-074), and components of the analytical work flow were developed and funded by a grant to RZS from the National Center for Preservation Technology and Training, which focused upon a similar bibliometric analysis for predictive modeling in archaeology.

References

Gandrud, Christopher. 2014. Reproducible Research with R and Rstudio. Book. The R Series. London: CRC Press.

Gray, Charles T., and Ben Marwick. 2019. “Truth, Proof, and Reproducibility: There’s No Counter-Attack for the Codeless.” Book Section. In Statistics and Data Science, 111–29. Communications in Computer and Information Science. https://doi.org/10.1007/978-981-15-1960-4_8.

Magee, Bryan. 1974. Popper. London: Frank Cass.

Peng, Roger D. 2011. “Reproducible Research in Computational Science.” Journal Article. Science 334 (6060): 1226–7. https://doi.org/10.1126/science.1213847.