The role of cooperative mediation in increasing the number of entrepreneurs: Case study of the DKI credit cooperative
Bibliographic record
Abstract
Even though the investment sector generally governs economic growth in developed coun-tries. Indonesia's economic growth so far has been dominated by the consumption sector. On the other hand, one of the significant contributors to the investment sector is the entrepreneurs’ role in any country. Therefore, the challenge for Indonesia is to make the entrepreneurs reach substantial contributions. This study investigates on three variables; namely entrepreneurial orientation, cooperatives, and entrepreneurs. Entrepreneurial orientation is the exogeneous variable, cooperative is the mediator variable and, the entrepreneur is the endogenous variable. These three variables are formed into three sub-models and one complete model. Each sub-model is to find out whether the dimensions contribute to each variable, and the entire model is to find out a significant relationship among the variables. The method used is the structural equation model (SEM), with computer software LISREL. This research will produce a breakthrough in how cooperative can mediate from entrepreneurial orientation to entrepreneurs. Besides, the study will deliver how cooperative stakeholders to get some understanding of entrepreneurial orientation so that cooperatives as an institution can persuade members of organizations to become entrepreneurs to be ready to contribute to economic growth. Moreover, the results provide the science of sustainable management on how to apply creativity and innovation to the cooperative organization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".