Methodology for Integral Evaluation of Human Development Level as Exemplified by the Leading Countries of Asia-Pacific Region and European Union
Bibliographic record
Abstract
The authors examine evolution of approach towards understanding of human capital and qualitativetransformation of human’s role in economics, conditioned by the transition to post-industrial society.Neo-classical approach has a special role in development of human capital theory. Examining human capital as asource economic growth of any country, the authors point out qualitative perfection of human potential. Thearticle introduces methodology for integral evaluation of level of human development exemplified by the leadingcountries of Asia-Pacific Region an European Union. In addition, complex evaluation of human capitaldevelopment is discussed as a two-side process: from one side, it is a formation of human abilities and skills,from the other side – realization of gained abilities and skills for the use of production or for recreation, cultural,political activity. Several key points of human development are pointed out for the purposes of a complexevaluation of human potential.Authors’ methodology of calculation of human development index is distinguishable from conventionalmethodology by at least one main principle: the authors believe that conducted experimental calculations mightprovide the most complete idea of human development from the point of view of three interconnectedcharacteristics: human capital, human resources, and evaluation of conditions for formation and implementationof abilities and skills of a person.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".