print.trail_record

internal

Tools for AI-assisted qualitative data coding using large language
models ('LLMs') via the 'ellmer' package, supporting providers including
'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'.
Provides a 'codebook'-based workflow for defining coding instructions and
applying them to texts, images, and other data. Includes built-in 'codebooks'
for common applications such as sentiment analysis and policy coding, and
functions for creating custom 'codebooks' for specific research questions.
Supports systematic replication across models and settings, computing
inter-coder reliability statistics including Krippendorff's alpha
(Krippendorff 2019, <doi:10.4135/9781071878781>) and Fleiss' kappa
(Fleiss 1971, <doi:10.1037/h0031619>), as well as gold-standard validation
metrics including accuracy, precision, recall, and F1 scores following
Sokolova and Lapalme (2009, <doi:10.1016/j.ipm.2009.03.002>). Provides audit
trail functionality for documenting coding workflows following Lincoln and
Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness
in qualitative research.

Seraphine F. Maerz

quallmer

Qualitative Analysis with Large Language Models

Kenneth Benoit

print.trail_record function

<dl><dt>x</dt>
<dd>A trail_record object.</dd>
<dt>...</dt>
<dd>Additional arguments passed to print methods.</dd></dl>

Arguments

Print a trail_record object — print.trail_record

<dl>

<dt>x</dt>
<dd>A trail_record object.</dd>


<dt>...</dt>
<dd>Additional arguments passed to print methods.</dd>

</dl>

print.trail_record: Print a trail_record object

Description

Usage

Value

Arguments