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The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations.
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X-RAY TOMOGRAPHIC MICROSCOPY AS A MEANS TO SYSTEMATICALLY TRACK EXPERIMENTAL DECAY AND FOSSILIZATION
ABSTRACT Laboratory-based decay experiments have become commonly used to supplement our understanding of how organisms enter the fossil record. Differences in how these experiments are designed and evaluated, however, including dissimilarities in qualitative decay-scoring indices superimposed on variability in model organisms, renders any semblance of comparison between studies unreliable. Here, we introduce the utility of X-ray tomographic microscopy (μCT) as a means for reliable and repeatable analysis of soft-tissue decay experiment products. As proof-of-concept, we used a relatively simple experimental design with classic studies as comparators, and present our analytical protocol using μCT for capturing the entire volume of the decay subject. Segmentation software then allows for 3D volume analysis and high-resolution internal and external character identification. We describe the workflow from sample preparation, contrast-staining, and data collection to processing and analysis of the resulting data, using peppermint shrimp (Lysmata wurdemanni) as model organisms, and compare our results to previous taphonomic studies. These methods allow for improved visualization and quantification of decay and internal volume analysis with minimal handling as compared to traditional qualitative scoring methods. Using the same scoring criteria as previous studies, this study revealed similar decay results for certain features, while we were additionally able to detect other feature loss or alteration earlier—importantly without need for potentially distortive sample handling. We conclude that μCT is a more effective, straightforward, and exact means for extracting quantitative data on the progression of decay and should be adopted in future studies, where available, to streamline and standardize comparisons.
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- Award ID(s):
- 1652351
- PAR ID:
- 10326940
- Date Published:
- Journal Name:
- PALAIOS
- Volume:
- 36
- Issue:
- 6
- ISSN:
- 0883-1351
- Page Range / eLocation ID:
- 216 to 224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2110/palo.2021.004
