Creative Leadership in Entrepreneuring People With Special Abilities
Bibliographic record
Abstract
During the 2020 COVID-19 pandemic, Indonesia experienced a significant decline in domestic economic growth, with a 2.97% drop in the first quarter and 5.32% in the second quarter. The pandemic has also resulted in a sharp decline in the community economy, especially for people with special abilities (PwSAs), namely those with apparent physical limitations and intellectual disabilities. To alleviate the economic pressure faced by PwSAs, it is crucial to foster their entrepreneurial spirit and skills through creative leadership. This paper portrays the visionary leadership of a village head in Indonesia in empowering and improving PwSAs’ economy through entrepreneurial activities. The research data was collected by observing the daily lives of PwSAs, interviews, and focus group discussions (FGD) with PwSAs and village officials. The study results show that the village head used different approaches for developing the entrepreneurship spirit and skills of PwSAs through training, skills guidance, and craft innovation before and during the pandemic. The programs implemented have increased the PwSAs’ economic level, reducing the number of poor people. This village head is clear evidence of the application of creative leadership in entrepreneurship with all his patience and empathy during the COVID-19 pandemic, which was finally able to solve complex problems faced by the PwSAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".