JD Coded Dataset for AJF Framework Study (v2: with cross-role validation N=62 and Persona×LLM simulation)

Version 2 update (2026-06-08): Added two supplementary appendix datasets for the SLTM 2026 paper. Appendix A — Cross-Role Radiation Validation (N=62, design 31 + engineering 16 + planning 15). Appendix B — Practitioner Persona × LLM × AJF 12-indicator simulation. The original 51-JD main dataset (v1) is retained. This dataset is the supplementary research data for the paper "An Exploratory Study on Changes in Art Competencies in the AI Era" (Lee & Tang, 2026, SLTM Conference). The main dataset contains 51 fully de-identified and coded job descriptions (31 current postings from 2025-2026 + 20 baseline postings from 2023-2024) from the Greater China region (Taiwan-primary, Hong Kong-secondary). Data is encoded according to the AJF (Awareness-Judgment-Formation) framework with four skill tiers (L1 Execution / L2 Application / L3 Integration / L4 Decision) and includes industry codes, region codes, platform-type codes, salary buckets, and AJF-classified skill counts. Original job description texts and identifiable company information are NOT included to comply with platform terms of service. Researchers seeking original data verification may contact the author under NDA. See CODEBOOK.md, METHODOLOGY.md, and README.md inside each archive for full documentation. --- 本資料集 v2 版本 (2026-06-08) 新增 SLTM 2026 論文兩份附錄補充資料:附錄 A — 跨職能輻射驗證 (N=62,設計 31 + 程式 16 + 企劃 15);附錄 B — 實務原型 Persona × LLM × AJF 12 指標模擬編碼。原 51 筆主資料集 (v1) 保留。 本資料集為論文〈AI 時代美術職能變化之探索性研究〉(李世彬、唐政元,2026,SLTM 智慧生活科技與管理研討會) 之佐證資料。資料集包含 51 筆完全去識別化編碼之職缺資料 (現況 31 筆 2025-2026 + 基線 20 筆 2023-2024),涵蓋大中華地區 (台灣為主、香港為輔)。資料依 AJF 框架 (感知覺醒-判斷結構化-職能重定位) 分至四層技能層級編碼,並包含產業、區域、平台類型、薪資區間等代碼。原始職缺文字與可辨識公司資訊未公開以遵循平台服務條款。