MEASURING NATIONAL ARTIFICIAL INTELLIGENCE CAPACITY: A HOLISTIC APPROACH BASED ON HUMAN CAPITAL SPECIALIZATION
Bibliographic record
Abstract
The transformative impact of artificial intelligence has made the need to acquire necessary skills for technology adoption and adaptation even more critical. This situation has highlighted the need for developing criteria, metrics, and methodologies to measure advancements in the field of artificial intelligence. The primary objective of this research is to determine the national level of specialization in the field of artificial intelligence. To achieve this goal, human capital elements such as knowledge base, skills, and sectoral experiences have been comprehensively examined. The research was conducted in three stages. In the first stage, a comprehensive conceptual framework was developed to examine the specialization level of human capital and related factors. This framework forms the theoretical foundation of the study. In the second stage, an original model was developed to measure the specialization level of human capital in the field of artificial intelligence. The AHP-Gauss method, a hybrid approach, was used in developing this model. In the final stage of the research, the developed model was used to analyze and comparatively evaluate the specialization level of human capital in AI across the countries included in the study scope. This stage enabled the empirical measurement of national artificial intelligence competencies and the establishment of a cross-country comparative assessment. The research findings reveal that the United States, the United Kingdom, India, Germany, and Canada are in leading positions in terms of human capital specialization levels in the field of artificial intelligence. The study's results emphasize the necessity of clarifying the strategic approach for developing human capital in the artificial intelligence domain and shed light on areas that require intensified focus in this context. These findings provide critically important data for the formulation of national artificial intelligence policies and the design of human capital development strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".