SOCIAL CAPITAL, BUSINESS ENVIRONMENT AND STATE INSTITUTIONS: THEORETICAL BASICS, PRACTICAL EXPERIENCE AND RECOMMENDATIONS (WITH EMPHASIS ON LEGISLATURE)
Bibliographic record
Abstract
Business social environment as one of environments of surrounding business was of great importance as a result of the economists perspective change, from emphasis on physical capital and then human capital to social capital. Despite these changes, we can say Iran governance has deemed that improving business environment is independence of promoting social capital, with looking at Development Plan Acts (fourth and fifth Plans) and Iran’s Vision 2025. This paper tries to answer these questions by the descriptive-analytical method and use of document method: what is the role of social capital in improving business environment? Suppose the social capital improves business environment, do state institutions can increase or decrease social capital by taking some process, making some policies and passing some acts. The latter question is important for creating a new perspective in Iran. Policy makers and legislators in Iran have accepted “the effect of state institutions on business environment”. The paper will discuss in a growing literature about the effect of state institutions on social capital and then business environment (social capital as an intermediate variable). At least, Canada and Spain’s experiences confirm the role of legislature in improving social capital is crucial. After analyzing associating social capital with business environment and the effect of state institutions on social capital, this paper will provide legislative recommendations for promoting social capital in Iran.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".