The Role of Intellectual Capital in Overcoming the Slowing Economic Growth in Indonesia
Bibliographic record
Abstract
Intellectual capital is one of the important factors that play a major role in various economic activities. However, its position in overcoming economic problems in Indonesia is still not too much considered. Based on this, this study aims to describe the role of intellectual capital, especially the Human Capital dimension, in overcoming the economic slowdown in Indonesia. The type of this research is qualitative. Research data in the form of secondary data is collected through documentation studies of data related to human capital and Indonesia’s economic growth, as well as the results of previous studies that have relevance to the topic of this study. The data is then analyzed using qualitative methods. The results of this study are: 1) Intellectual capital has a fundamental role in overcoming the economic slowdown in Indonesia. One dimension of intellectual capital, namely human capital, can have a diverse role, both as a factor of production and as an economic policy maker, so that its existence and quality determine the success of the strategy formulation and its implementation to overcome the economic slowdown in Indonesia; and 2) The role of intellectual capital, especially human capital, can be realized if Indonesia changes its development paradigm to become more oriented towards the development of human resources quality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".