Reproducible analysis archive for Higher aggregate perceived explanation-necessity scores are associated with longer human-written explanations of autonomous driving scenes
Year: 2026
Type: software
Abstract: This record provides version 2.0 of the reproducibility software and raw-text-free derived results supporting the study “Higher aggregate perceived explanation-necessity scores are associated with longer human-written explanations of autonomous-driving scenes.” The archive includes a Dockerized analysis environment, input-provenance documentation, machine-readable results, and code for bounded score-model comparisons, score–length analyses, record-weighted and unique-template TF-IDF lexical analyses, co-occurrence networks, template-grouped out-of-fold text classification, sensitivity analyses, and generation of the main and supplementary figures. The named score partitions are used as exploratory descriptive strata and should not be interpreted as validated psychological categories. The original explanation-necessity label file and source videos are not redistributed. Reproduction requires explanation_necessity_labels.txt obtained through the source project; instructions for acquiring and verifying the exact analyzed file are provided in the README. Code is licensed under the MIT License. Author-created documentation, figures, and raw-text-free derived outputs are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). These licenses do not cover the external labels, videos, proprietary fonts, or third-party software.
Institution: Gwangju Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204031308