BBC and NHK trust reproduction code and aggregate materials

Abstract

Companion Python code, aggregate numerical outputs, figures and reproducibility instructions for How Robust Are Political Differences in Trust in Public Service Broadcasters? A Same-Data Reproduction and Threshold Analysis of BBC and NHK Surveys. Version 1.0.0 contains 64 listed components; source input hashes, dependency versions and the run order are documented. The original microdata, original R script, dependency packages and private author/administrative records are excluded. Inputs must be obtained from Harvard Dataverse DOI 10.7910/DVN/AUE81C version 1.0. The original study is Pickering, Sunahara and Hansen (2025), DOI 10.1515/commun-2024-0184. Human contribution and creator metadata. Nguyen Minh Quan refined the aims and the focus on the complete distribution of trust, directed revisions and completion gates, and specified three methodological safeguards: no model selection for favourable p-values; treating wave sensitivity as assumption-based rather than as verified survey-design inference; and keeping conclusions within the evidence. His confirmed CRediT roles are Conceptualization, bounded Methodology, Project administration, and Writing – review & editing. The author has confirmed personal review and understanding of the methods, results tables and confidence intervals, the ability to explain them, and review of the approved scientific and reproducibility dossier with responsibility for its accuracy, honesty and content rights within the scope he can confirm. This does not establish that he personally wrote or ran the code, independently recalculated every result, or performed a separate line-by-line human code audit. Nguyen Minh Quan is identified as the human responsible for intellectual direction and release stewardship; that creator designation does not claim that he personally programmed this software. Substantive AI execution. OpenAI ChatGPT and ChatGPT Work were used substantially for source reading and checking, technical methodological implementation, Python code development and execution, statistical calculations and numerical verification, tables and figures, initial drafting, editing and internal adversarial review. The technical implementation, computation and recorded computational checks were carried out through this AI-supported workflow; they are not credited as the author's personal Software, Formal analysis, Validation or Writing – original draft. Recorded workflow labels identify GPT-6/ChatGPT where documented; exact model versions for all earlier sessions cannot be independently recovered, and a single version throughout is not asserted. The computations use the original empirical survey inputs identified by Pickering (2024), Harvard Dataverse DOI 10.7910/DVN/AUE81C, version 1.0; the recorded checks connect outputs to those inputs. The aggregate results are computed analyses of those inputs, not synthetic respondent observations or invented empirical findings. Computational checks through AI are not external human peer review or journal certification. No AI tool is an author, creator or contributor; tools are disclosed here, not entered in those metadata fields. AI use extends to the analytical software and numerical checks, not merely editing the instructions. Retained verification evidence supports the reported computations but does not convert AI-performed technical checks into independent human validation. No fresh model fitting or new scientific result was performed during this release correction. The exact component license map remains FILE_LICENSES.csv: MIT for new licensable code/instructions, CC BY 4.0 for aggregate reporting/figures within applicable rights, and CC0 for owned factual/bibliographic metadata. Upstream rights remain unchanged. Survey-design information, assumptions about dependence, exploratory selection history and verification limits are retained. This is a reviewed research companion archive, not a deposit of unreviewed chat transcripts or a claim of a journal-awarded reproducibility badge.