Architecture of a test structure for verification of libraries of standard elements in silicon based on a pipeline-distribution approach
Bibliographic record
Abstract
This paper presents a comparative analysis of two architectures for decision support systems (DSS) aimed at adapting the content of educational programs to current labor market demands. The first architecture relies solely on the RuBERT language model to semantically match competencies from educational programs with requirements extracted from job postings. The second architecture extends this approach by integrating an ontological knowledge graph of skills and occupations based on the ESCO taxonomy. Experimental evaluation was conducted using real-world data from a Russian undergraduate program in applied informatics and a corpus of IT-related job vacancies. The results show that while the RuBERT-based architecture achieves high semantic matching accuracy, the ontology-enhanced system provides greater coverage of relevant skills (75% vs. 65%), more interpretable recommendations, and the ability to perform logical inference, such as identifying missing skill categories and aligning educational profiles with specific occupations. The incorporation of the Revealed Comparative Advantage (RCA) metric enables prioritization of curriculum updates based on skill demand intensity. The scientific novelty lies in the integration of pre-trained language models with a formalized skill ontology for curriculum design. The practical significance is demonstrated by the system’s ability to support curriculum developers, educational administrators, and accreditation bodies in continuously aligning educational content with dynamic labor market needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".