Bibliographic record
Abstract
This research is focused on achieving green growth through an environmental technology approach. Developing environmental technology we examined four elements considering the enforcement of intellectual property rights (IPRs), research and development (R&D) expenditures, the size of the market capture by GDP and most importantly the environmental taxations. This study includes the 11 developed countries which are Austria, Australia, Canada, France, Japan, Finland, Germany, Sweden, U.K and U.S. Technology change can be better handled by panel data than by pure cross-section or pure time series. It can minimise the bias if we used the aggregate individuals or firms. Estimation techniques depend on short panel or long panel. This study used the Pooled Least Square estimation techniques like Fixed Effect Model (FEM) and random effect model (REM) for both balance period of 2000-2005 and unbalanced period from 1995-2005. The study concluded the policy formulation in making developed‘s climate resilient economies. JEL classification: O34, F19, L24 Keywords: Intellectual Property Rights, Foreign Direct Investment, Technology Licensing
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".