The analysis of convergence — Divergence in the development of innovative and technological processes in the countries of the Arctic Council
Bibliographic record
Abstract
The article gives the estimation of the convergence-divergence indicators of development of innovative and technological processes in circumpolar countries. The processes of convergence - divergence of innovative and technological processes were studied in eight countries of the Arctic Council - Canada, Denmark, Finland, Iceland, Norway, Russia, Sweden, the United States of America on the basis of statistical information from 1985 to 2015. As the indicators, measuring innovative and technological processes, the following one were considered: the number of patents issued, the expenditures on technological innovations, the payments of funds for the import of technology, the cash inflow from the export of technology. To analyze the convergence-divergence of innovative and technological processes the methods of the σ - convergence, the absolute β convergence and the conditional β - convergence were used. Using the method of the conditional β - convergence, we analyzed the impact of foreign direct investment (FDI) and government expenditure on the fundamental research and development (average value of FDI for the period; the average value of government expenditure over the period).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".