Human Capital on the Knowledge Economy: The Role of Continuing Education
Bibliographic record
Abstract
In economy, driven by innovations, the society is developed by the combination of exchange process and knowledge use in the manufacturing and in other spheres as well. The creation, accumulation and effective use of knowledge play a significant role in this process. One of the most important factors that define the forming of intellectual capital, its development and rational use is professional education. The present article analyzes such categories as capital, intellectual capital, its denomination and classification. The authors highlight continuing education as the fundamental factor establishing intellectual capital and the features of the adult workers in the aspect of education. The present article provides a quick review of several strands of a literature of contemporary Russian and foreign researchers and historical examples of the end of the XIX century that provide evidence in support of the topic. Furthermore, the paper represents calculation of KPIs in education as the key factor that creates intellectual capital. Highlights: One of the key factors that denominates significance and use of intellectual capital is education. Productivity of more educated workers is higher than that one of less educated staff. We highlighted features of continuing professional education which influence the intellectual potential of an experienced worker. It has been proved that real monetary return on higher education considerably differs for men and for women. Thus, continuing education is a key factor influencing the salary, a job attitude, productivity growth and also an economy’s ability to develop.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".