Positive and Negative Effects of Research and Development
Bibliographic record
Abstract
This study aims to examine the effects of research and development expenditure on economic growth and carbon dioxide emission in the panel data for the period of 1996-2011 from 5 nations (Germany, Russian Federation, United Kingdom, United States and Canada). The panel co-integration was conducted and the results show that there is co-integrated relationship among the variables (R&D, GDP, Energy Use and Carbon Dioxide Emission). Then the FMOLS test was performed and the findings explain that energy use and R&D are the determinants of GDP. Results from FMOLS show that energy use and R&D are the determinants of GDP. Energy use and GDP are the determinants of carbon dioxide emission. Results from DOLS show that R&D is important to boost economic growth while energy use, GDP and R&D can have deleterious effects on carbon dioxide emission. Therefore, it is important to control the R&D expenditure to balance economic growth and environmental conservation.
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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 | 0.000 | 0.000 |
| 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".