Az intellektuális tőke VAIC™-alapú hatékonysága és a jövedelmezőség kvantilis panel-elemzése a visegrádi országokban és Romániában (2015–2019)
Bibliographic record
Abstract
The aim of the study is to examine the efficiency of intellectual capital in companies from the Visegrad Four countries and Romania. The study investigates the development of intellectual capital efficiency in large companies from five countries, focusing on the components of the VAIC indicator. Furthermore, using panel regression, the study analyzes the impact of the elements of the VAIC indicator on profitability indicators considered by investors. The results of the research indicate that most of the independent variables have an impact on profitability indicators. The effect was stronger for companies with lower profitability indicators, while smaller changes were observed in companies with higher profitability. Additionally, it was found that human capital had the greatest impact on profitability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.019 |
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; both teacher heads agree on what is shown here.
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".