Green Growth: An Environmental Technology Approach.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.003 |
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 it