INTELLECTUAL COMPONENT OF GLOBAL LEADERSHIP
Bibliographic record
Abstract
The rapid intellectualization of economic activity in modern conditions actualizes the study of issues of countries’ leadership, achieved with the help of intellectual factors. The article’s purpose is to assess the intellectual leadership of countries based on the performance indicators of intellectual activity. The method of estimation of intellectual leadership is proposed, which provides three levels of evaluation based on the corresponding set of indicators. The three-step evaluation algorithm includes, firstly, the assessment of the intellectual potential of the resource potential, secondly, the consideration of the results of intellectual activity and, thirdly, the final results of economic activity. The second level indicators are the result of intellectual activity, the implementation of intellectual resources and at the same time, they become the basis for further intellectualization. The analysis of the performance indicators of the intellectual activity of the countries of the world made it possible to identify the composition and disposition of the world leaders (the USA is the leader by nine indicators, Japan and Germany are leading by six, South Korea, the United Kingdom, China and France - by 5, Switzerland and Canada - 4 indicators) and showed a high volatility of their ranks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".