Phyllis S Johnson
Biographic Data
| ID | 933905 |
|---|---|
| NAME | Phyllis S Johnson |
| GIVEN NAMES | Phyllis S |
| FAMILY NAME | Johnson |
| SIGNATURE | JOHNSON P S |
| AFFILIATIONS | Augustana University |
| ORCID | 0000-0001-9826-6189 |
| VERIFIED | Yes |
| TOTAL WORKS | 12 |
| TOTAL CITATIONS | 16 |
| AUTHOR COUNT | 12 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Ethical and environmental considerations for the application of Artificial Intelligence in archaeological research
Artificial intelligence (AI) has been making big waves in archaeological research over the past decade, with an increasing number of archaeologists implementing generative AI, machine learning, and deep learning technologies into their research programs. Though AI can improve the efficiency and accuracy of archaeological research, few archaeologists have considered the environmental impacts of AI. As stewards of cultural and environmental resourc…
Putting the People before the Work
Cultural resource management (CRM) in the United States is an industry that thrives (or falters) on tight budgets and short deadlines. To steadily secure a reliable stream of projects from major clients (and to support the limited budgets of smaller clients), CRM firms are often forced to work with bare-bones funding, requiring them to demand a great deal of effort from their field and office personnel to stay within budget and on schedule. When …
Patriarchy persists
Gender discrepancies among peer-reviewed publications in academia have become a topic of great interest to archaeologists over the past two decades. Overall, this research has demonstrated a consistent bias towards men, who tend not only to publish more often but are also much more likely to serve as first or sole author. In the current study, we examine publishing trends in the peer-reviewed, four-field journal Plains Anthropologist between 1954…
Macrodebitage Writ Small? Studying Stoneknapping with Experimental Archaeology, Dynamic Image Analysis, and Statistics
Centering Indigenous Approaches for a Better Cultural Resource Management
The Dakota Access Pipeline (DAPL) has been one of the most nationally publicized of all protested pipelines in the United States. Since protests began at Standing Rock in November 2016, injustices to the natural and cultural environment inflicted by its construction have been scrutinized by the public, the media, archaeologists, and perhaps most of all, by Tribal nations residing in this area and throughout the United States. In this article, I f…
Machine Learning–Based Identification of Lithic Microdebitage
Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We…
Redefining lithic microdebitage with experimental archaeology
Examining Gender Disparities in Computational Archaeology Publications
Article: Examining Gender Disparities in Computational Archaeology Publications: A Case Study in the Journal of Computational Applications in Archaeology and the Computer Applications and Quantitative Methods in Archaeology Conference Proceedings
Using tiny artifacts to answer big questions
The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take …
Studying lithic microdebitage with a dynamic image particle analyzer
Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image…
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples
Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protoco…
In much smaller things forgotten
The past several decades have seen a shift in archaeology from the exclusive analysis of “interesting” artifacts, such as diagnostic lithics and ceramics, towards a more holistic examination that includes the smaller, less obvious, “forgotten” artifacts. These micro-sized artifacts are a focus for archaeologists because many studies show their utility in documenting activity areas and site formation processes. As early as the 1970s, researchers r…
In much smaller things forgotten
The past several decades have seen a shift in archaeology from the exclusive analysis of “interesting” artifacts, such as diagnostic lithics and ceramics, towards a more holistic examination that includes the smaller, less obvious, “forgotten” artifacts. These micro-sized artifacts are a focus for archaeologists because many studies show their utility in documenting activity areas and site formation processes. As early as the 1970s, researchers r…
Studying lithic microdebitage with a dynamic image particle analyzer
Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image…
Machine Learning–Based Identification of Lithic Microdebitage
Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We…
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples
Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protoco…
Redefining lithic microdebitage with experimental archaeology
Using tiny artifacts to answer big questions
The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take …
In much smaller things forgotten
The past several decades have seen a shift in archaeology from the exclusive analysis of “interesting” artifacts, such as diagnostic lithics and ceramics, towards a more holistic examination that includes the smaller, less obvious, “forgotten” artifacts. These micro-sized artifacts are a focus for archaeologists because many studies show their utility in documenting activity areas and site formation processes. As early as the 1970s, researchers r…
Using Dynamic Image Analysis as a Method for Discerning Microdebitage from Natural Soils in Archaeological Soil Samples
Unlike larger stone tools and debitage, the analysis of microdebitage (measuring less than 6 mm) allows for identifying likely areas where stone tools were manufactured at prehistoric archaeological sites. The tedious, time-consuming, and costly nature of microdebitage, however, has overshadowed its data potential, making most archaeologists wary of implementing this method. To alleviate these issues, this study introduces an experimental protoco…
Examining Gender Disparities in Computational Archaeology Publications
Article: Examining Gender Disparities in Computational Archaeology Publications: A Case Study in the Journal of Computational Applications in Archaeology and the Computer Applications and Quantitative Methods in Archaeology Conference Proceedings
Using tiny artifacts to answer big questions
The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vulnerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take …
Studying lithic microdebitage with a dynamic image particle analyzer
Lithic microdebitage has great archaeological potential to elucidate ancient stone tool production. So far, archaeologists have collected soil samples, separated them into size fractions, and analyzed them manually under a microscope to identify microdebitage. This time- and labor-intensive process has limited the number of samples and introduced intra- and inter-observer errors. Here, we discuss lithic microdebitage analysis with a dynamic image…
Centering Indigenous Approaches for a Better Cultural Resource Management
The Dakota Access Pipeline (DAPL) has been one of the most nationally publicized of all protested pipelines in the United States. Since protests began at Standing Rock in November 2016, injustices to the natural and cultural environment inflicted by its construction have been scrutinized by the public, the media, archaeologists, and perhaps most of all, by Tribal nations residing in this area and throughout the United States. In this article, I f…
Machine Learning–Based Identification of Lithic Microdebitage
Archaeologists tend to produce slow data that is contextually rich but often difficult to generalize. An example is the analysis of lithic microdebitage, or knapping debris, that is smaller than 6.3 mm (0.25 in.). So far, scholars have relied on manual approaches that are prone to intra- and interobserver errors. In the following, we present a machine learning–based alternative together with experimental archaeology and dynamic image analysis. We…
Redefining lithic microdebitage with experimental archaeology
Macrodebitage Writ Small? Studying Stoneknapping with Experimental Archaeology, Dynamic Image Analysis, and Statistics
Ethical and environmental considerations for the application of Artificial Intelligence in archaeological research
Artificial intelligence (AI) has been making big waves in archaeological research over the past decade, with an increasing number of archaeologists implementing generative AI, machine learning, and deep learning technologies into their research programs. Though AI can improve the efficiency and accuracy of archaeological research, few archaeologists have considered the environmental impacts of AI. As stewards of cultural and environmental resourc…
Putting the People before the Work
Cultural resource management (CRM) in the United States is an industry that thrives (or falters) on tight budgets and short deadlines. To steadily secure a reliable stream of projects from major clients (and to support the limited budgets of smaller clients), CRM firms are often forced to work with bare-bones funding, requiring them to demand a great deal of effort from their field and office personnel to stay within budget and on schedule. When …
Patriarchy persists
Gender discrepancies among peer-reviewed publications in academia have become a topic of great interest to archaeologists over the past two decades. Overall, this research has demonstrated a consistent bias towards men, who tend not only to publish more often but are also much more likely to serve as first or sole author. In the current study, we examine publishing trends in the peer-reviewed, four-field journal Plains Anthropologist between 1954…
Archaeology (8 works) · Image Processing and 3D Reconstruction (8 works) · Computer Science (7 works) · Archaeology and ancient environmental studies (5 works) · Geography (5 works) · Geology (4 works) · History (4 works) · History (4 works) · Pleistocene-Era Hominins and Archaeology (4 works) · Archaeological Research and Protection (3 works)