National Intellectual Capital and Economic Performance: Empirical Evidence from Developing Countries
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
The aim of this study was to examine the relationship between national intellectual capital and economic performance in less developed countries. This study develops, measures, and tests a general model of the interrelationship among selected sub‐components of national intellectual capital and its impact on economic performance in 148 developing countries. The results indicate that national intellectual capital explains 70 per cent of the variance in economic performance in developing nations. Findings also indicate that national relational capital is a critical component in achieving economic performance. A variety of sub‐hypotheses were also tested and compared with those of the previous studies that focused on developed nations. The findings of this investigation contribute to the growing theory of national intellectual capital management by providing empirical evidence of the interrelationship among sub‐components and their impact on economic performance. Investigating national intellectual capital provides insights into the derivative of economic performance in less developed countries. This helps policy makers to rethink the economic development drivers of their countries and to formulate strategies that leverage unique sources of competitiveness. Copyright © 2013 John Wiley & Sons, Ltd.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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