Exploring the interplay of entrepreneurial leadership and knowledge sharing strategies in territorial development, national defense, and strengthening resilience
Bibliographic record
Abstract
This research aims to investigate the factors that affect the performance of national resilience, which are suspected to be related to the elements of territorial entrepreneurship leadership, knowledge sharing, territorial development, and national defense intentions. The sample consisted of 278 inmates in the context of national resilience. Primary data was obtained through the distribution of questionnaires to the sampled respondents. The data was analyzed using the Structural Equation Modeling (SEM) technique with the help of the Analysis of Moment Structure (AMOS) software. The results of the data analysis indicate that the Territorial Entrepreneurial Leadership Strategy implemented thus far does not have a significant impact on the Territorial Development Strategy, State Defense Intentions, and National Resilience Performance. However, on the other hand, the Knowledge Sharing Strategy contributes significantly to enhancing the Territorial Development Strategy and National Resilience Performance. Similarly, it has been observed that the National Defense Intention plays a crucial role in improving National Resilience Performance. It can be concluded that the development of territorial communities needs to consider various strategies and factors, including leadership strategy, knowledge exchange, and national defense awareness, in order to achieve regional development goals and enhance national resilience comprehensively. Collaboration among local leaders, communities, and other stakeholders is key to designing and implementing effective programs in this context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".