An empirical analysis of causal nexus between higher education and economic growth in BRICS countries
Bibliographic record
Abstract
This study examines the connection between higher education and economic development in the BRICS countries. This study used the gross enrolment ratio (GER) to gauge the extent of higher education in the BRICS countries, while gross domestic product (GDP) was used to estimate economic growth. The study includes both time series and panel data analysis for all BRICS countries. The vector error correction model (VECM) and the vector autoregressive model (VAR) were applied to time series data to explain the causal relationship between GDP and GER. Panel data were analyzed using the panel vector auto-regression (PVAR) model. The time series analysis revealed that there is both a bi-directional and a unidirectional causal link between economic development and higher education. However, panel data analysis revealed that the causal influence of higher education was more pronounced than economic growth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".