Strategic Management Model for Legal Entity State Universities Toward a World Class University (WCU) Through a Strategic Intelligence Approach
Bibliographic record
Abstract
Background: In the present era of global industrialization, universities are facing an increasing challenge to nurture human resources that are capable of thriving and excelling in global competition. Universitas Sumatera Utara (USU) serves as one of the largest universities in Indonesia and is counted among the 16 Legal Entity State Universities (LESU). As part of the strategic plan outlined by the relevant ministry, there is a priority program that encourages Indonesian universities to achieve the World Class University (WCU) predicate. This scenario necessitates an enhancement of academic reputation Internationally. Method: An explanatory with a quantitative approach was used, and the data were analyzed using Structural Equation Models (SEM). Results: Consequently, the result showed that strategic intelligence significantly and positively played a role in accelerating the influence of strategic management on the performance of the university in achieving WCU predicate. Typically, strategic management consists of strategy formulation, implementation, evaluation, and control. Conclusion: Strategic intelligence, including the upstream system, input quality, leadership system quality, and sound policy guidance, could be recommended as an appropriate approach to strengthen the capacity and expedite the achievement of USU's targets for the WCU predicate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".