Bibliographic record
Abstract
Innovations in developed countries also affect the economic growth of developing countries. Developing countries cannot allocate sufficient budget for R&D expenditures because their capital is less. For this reason, developing countries, which are insufficient in terms of capital, generally acquire new technologies with foreign direct investments and make improvements in their own production techniques, thus accelerating their international trade in terms of exports. For this reason, improvements in the innovative performance of developed countries are also important for the economic development of developing countries. In this study, it is aimed to compare the G7 countries, which are the leading country group, America, Germany, England, Japan, Canada, France and Italy, in which selected innovation indicatorsbetween 2012 and 2020, which indicators should be improved and the increasing effects of these indicators on their exports. In this direction, high-tech product exports, R&D expenditures, gross capital formations, government efficiency indices, education indices, information and communication technologies use indices, GDP growth rates and export figures, which are among innovation indicators, were analyzed comparatively. In the light of the evaluations, being the leader in R&D expenditures and the index of information and communication technologies use in G7 countries does not increase high technology exports. However, it has been determined that the increase in the education index rate, which is one of the innovation indicators in the G7 countries, has an impact on the increase in high technology exports.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".