Reproducibility Package for: Human-Study Dataset and Reproducibility Package for Staged Multi-Criteria Disclosure in ML Model Selection

This record documents the underlying human-study dataset and reproducibility materials for a staged multi-criteria disclosure study in ML model selection. The archived materials were prepared by Adil Joldić and Nina Bijedić and support multiple derived publications based on the same broader study, with version-specific processed files corresponding to different analytic snapshots and paper-level analyses. The record includes processed study data, frozen policy and stimulus specifications, and reporting artifacts needed to verify published analyses. It does not include the full live study application or the complete agent system codebase; instead, it focuses on the processed study data, frozen scenario specifications, and analysis artifacts required for paper-level reproducibility. Derived publications based on this broader study include: Joldić, A., Bijedić, N., Gašpar, D., & Mabić, M. (2026). From Unaided Choice to Multi-Criteria Disclosure: A Human Study of ML Model Selection in Higher-Education Quality Assurance. ISDA 2026. Joldić, A., Bijedić, N., & Gašpar, D. (2026). Behavioral Evidence from Staged Multi-Criteria Disclosure: A Companion Human-Study Analysis of ML Model-Selection Deliberation in Educational QA. ICIICE 2026. Joldić, A., & Bijedić, N. (2026). Human-Centred Acceptance and Selective Reliance in AI-Assisted Decision Support for Higher-Education Quality Assurance. TILE-TEC 2026. This archived version corresponds to the locked public reproducibility snapshot tied to export 20260314_003853Z. It represents the paper-level frozen dataset package used for confirmatory and sensitivity analyses reported from that snapshot, including the ISDA 2026 paper and companion analyses derived from the same broader study. The locked snapshot contains: 38 participants / 228 confirmatory scenarios 90 participants / 539 sensitivity scenarios It includes: anonymized confirmatory and sensitivity scenario-level datasets, frozen criterion weights and frozen stimulus payload export, exported statistical summaries, a reproduction script and README. This version is intended to support reproduction of reported tables, descriptive summaries, and key inferential results from the frozen export snapshot, while also documenting the shared empirical basis of the companion ISDA 2026, ICIICE 2026, and TILE-TEC 2026 publications.本数据集记录了一项针对机器学习（Machine Learning）模型选择的分阶段多准则披露研究的原始人类受试者研究数据集与可复现性材料。该归档材料由Adil Joldić与Nina Bijedić整理，可支撑基于该同一核心研究的多篇衍生论文发表，其中包含对应不同分析快照与论文级分析的版本化处理文件。 本数据集包含处理后的研究数据、固化的实验范式与刺激材料规范，以及验证已发表分析所需的报告产出物。本数据集未包含完整的在线研究应用程序与完整的AI智能体系统代码库，而是聚焦于支撑论文级可复现性所需的处理后研究数据、固化的实验场景规范与分析产出物。 基于本核心研究的衍生论文包括： 1. Joldić, A., Bijedić, N., Gašpar, D., 与 Mabić, M. (2026). 从无辅助选择到多准则披露：高等教育质量保障中机器学习模型选择的人类受试者研究. ISDA 2026 2. Joldić, A., Bijedić, N., 与 Gašpar, D. (2026). 分阶段多准则披露的行为证据：教育质量保障中机器学习模型选择决策审议的配套人类受试者研究分析. ICIICE 2026 3. Joldić, A., 与 Bijedić, N. (2026). 高等教育质量保障人工智能辅助决策支持中的人本接受与选择性依赖. TILE-TEC 2026 本归档版本对应于标识符为20260314_003853Z的锁定公开可复现快照。该快照代表了用于该快照中报告的验证性与敏感性分析的论文级固化数据集包，涵盖ISDA 2026论文及基于同一核心研究衍生的配套分析内容。 该锁定快照包含： - 38名受试者 / 228个验证性场景 - 90名受试者 / 539个敏感性场景 本数据集包含： - 匿名化的验证性与敏感性场景级数据集 - 固化的准则权重与固化刺激材料导出文件 - 导出的统计汇总结果 - 复现脚本与README文档 本版本旨在支撑该固化导出快照中报告的各类表格、描述性汇总结果与关键推论结果的复现，同时记录了ISDA 2026、ICIICE 2026与TILE-TEC 2026三篇配套论文的共享实证基础。