Scenario Analysis of the Intellectual Capital Development of the Region in Postcovid Economics Based on Financial Assets
Bibliographic record
Abstract
The article presents the main trends and factors involved in developing intellectual capital in the region during and after the pandemic of Covid-19 based on financial assets, which are of strategic importance both for the state and for international development. The authors provide benefits and drawbacks to the growth of the situation in the context of the main elements of the region's intellectual capital. These factors consist of Innovative activities in the region (Intellectual Property Market, Innovative infrastructure of the region, Innovative activities of organizations) and Human Resources (Labor Market, Higher, and Continuing Professional Education System). A pessimistic scenario involves that the state and the economic system could not cope with a negative trend for various reasons. On the other hand, an optimistic scenario implies that the state and economic entities can see the trend as an opportunity and apprehend it to stabilize the region's economic situation and further surmount the crisis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".