Data and code for: Label provenance in published artificial intelligence for tax and customs administration: a systematic review

Data, decisions and code behind the systematic review "Label provenance in published artificial intelligence for tax and customs administration: a systematic review" (Sushanta Paul, National Board of Revenue, Bangladesh). The review audits how published evaluations of AI in tax and customs administration obtained their ground-truth labels, how the labelled cases were selected, and whether the headline claims are supported by that evidence. The primary analysis covers 196 studies: 91 from the first search phase and 105 added by a later search. AI involvement. The first coding of every study was done by Claude. Every later step that the OSF registration (doi:10.17605/OSF.IO/2ANXY) assigned to humans was done by a panel of five language models from five vendors (openai/gpt-6-astra, google/gemini-4-argon, x-ai/grok-4.7, deepseek/deepseek-v4-pro-0813, qwen/qwen3.8-max-0902) under rules written before each run: title-and-abstract screening, the full-text eligibility check, blind adjudication of disputed codes, the second coder and the resolution of second-coder disagreements. No human took part in those steps, so the agreement figures for the main coding are agreement between language models. The one human check, the author's blind re-coding of 25 studies added on 30 October 2026, is described under Version 1.1.0 below. The protocol and its two addenda are in work/. Contents: search and screening records and decisions; eligibility decisions with quoted evidence; coding data with verbatim quotes and page numbers; the codebooks; screening sheets, model proposals and keys; the AI-completion protocol and addenda; adjudication, second-coder and resolution inputs and results; the panel's votes, parsed answers, run logs and manifests (prompt hashes, model identifiers, settings); analysis scripts, output tables and figures. README.md lists every folder and how to rerun the analysis. Not included: the full texts of the studies and the text extracted from them (copyright; each study is identified by DOI in the paper's supplementary material and in work/); the raw API response files, which stay with the author (the parsed answers, run logs and manifests are included); API keys. The files are in one zip archive; README.md lists the contents and how to rerun the analysis. Licences: data, documentation and figures CC BY 4.0; code MIT. Version 1.1.0 (30 October 2026) adds a second archive, author-check-1.1.0.zip: the author's blind re-coding of 25 of the 196 studies (plan filed on the OSF registration before coding; the sheet as submitted, the sealed key of model codes with its SHA-256, the scoring script, agreement tables and the 54 differences), and a re-code of the same studies by five blind Claude instances, labelled as not human. The author check is a human check but not an independent second coder, because the author wrote the codebook. The first archive is unchanged. The full texts of the 25 studies are not included (copyright).