PREDICTING THE GDP OF THE NEW ECONOMY BASED ON THE HUMAN CAPITAL USING NEURAL NETWORK APPROACH
Bibliographic record
Abstract
Human capital has become important because knowledge is a critical ingredient for gaining competitive advantages, particularly in the New Economy era.It has been described as becoming the preeminent resource for creating economic wealth. To date, several studies have been conducted to determine the relationship between human capital and company performance.The relationship between human capital and economic growth has been explored.However, past literature reveals that artificial intelligence techniques have not been utilized in understanding the effect of human capital on economic growth.Artificial intelligence techniques such as neural networks have been successfully applied to business and financial problems.To this end, the neural networks approach was used to determine the impact of human capital on the New Economy.This paper discusses the results of the exploratory study for predicting demand for human capital. Data from 1971 to 1996 was collected for this study.The variables used for the prediction were based on Canadian’s Human Capital Measurement as suggested by Laroche and Merrette (2000).The exploratory study indicated that neural network is a potential approach for predicting the GDP based on human capital. In conjunction with neural network approach, statistical methods were also used to explain the relationships between variables in the study.
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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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 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".