Bibliographic record
Abstract
This paper examines the impact of the human capital composition of unskilled, skilled, and high-skilled levels on the innovation capacity of middle-income economies. Data from 65 countries in lower middle-income, upper middle income, and high-income categories over the period of 1985-2019 is used. Panel data regressions are employed. Results suggest the innovation capacity enhancing effects of high-skilled human capital in upper-middle income countries (UMICs) and high-income countries. For lower middle-income countries (LMICs), the skilled human capital is the important workforce fostering their innovation capacity, while the R&D personnel of high-skilled human capital is yet to be important. Unskilled human capital is confirmed to not play any role in innovation development in MIEs and above. For UMICs, high-skilled human capital is supported by the foreign innovation diffusion through imports, and R&D capital stocks; while for LMICs, FDI-embodied foreign innovation supplements the skilled human capital to build up innovation capacity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".