Supplementary materials for: Portfolio-Scale Semantic Alignment of Computing Curricula with ACM/IEEE Guidelines and a Common Competency Axis

Data, alignment maps, reliability materials, and source code reproducing the figures and tables of the manuscript. The study measures how completely five accredited undergraduate computing programs of one college (Computer Science, Information Technology, Data Science and Artificial Intelligence, Information Security, and Computer Engineering) cover the ACM/IEEE bodies of knowledge that govern them (CS2023, IT2017, CCDS2021, CSEC2017, and CE2016), and projects all five onto the thirty-four-area Computing Curricula 2020 (CC2020) competency axis so that programs governed by different guidelines become comparable. Each program is read through topical and knowledge-unit coverage, competency-tier coverage, and cognitive depth, with an emphasis lens against recommended allocations. The pipeline pairs a benchmarked mean-cosine ensemble of six sentence-embedding retrievers with human confirmation against an explicit inclusion rubric, and reports reliability through blind intra-rater re-rating with independent inter-rater agreement for the antecedent Computer Science map (common-axis Cohen's kappa 0.821). Includes the structured guideline corpora, the five-program course corpus, the five human-confirmed program maps and the direct CC2020 common-axis map, the per-program and common-axis reliability instruments and responses, the extraction-accuracy validation, the end-to-end pipeline scripts, and the manuscript figures as vector PDF. See README.md for a full manifest and reproduction instructions. The raw per-model similarity caches are regenerable from the pipeline and are omitted for size.