Guidelines for Innovative Leadership Development of Private Vocational College Administrators in the Northeastern Region
Bibliographic record
Abstract
Innovative leadership development can assist administrators of private vocational colleges in identifying and capitalizing on new opportunities within the educational administration network by fostering creative thinking and openness to new ideas. Such administrators can discover novel approaches to efficiently and effectively address the needs of students, faculty members, and stakeholders. Therefore, the research objectives are as follows: to examine the components and indicators of innovative leadership among administrators of private vocational colleges in the northeastern region, to assess the consistency of the innovative leadership measurement model, and to develop guidelines based on the study findings for implementation. To develop the innovative leadership of private vocational college administrators, a mixed-method research approach was employed, consisting of four phases. The collected data were analyzed using descriptive statistics and statistical packages for further reference. The results revealed that Innovative Leadership comprises five main components and fifteen indicators. These indicators were found to be appropriate based on the specified criteria. The developed measurement model for innovative leadership indicators demonstrated consistency with the empirical data, with statistically significant values (P-value = 0.73, RMSEA = 0.023, SRMR = 0.019, CFI = 1.00, TLI = 1.00). Furthermore, all main components exhibited factor loadings higher than the criterion of 0.70, while sub-components and indicators displayed factor loadings higher than the criterion of 0.30. Finally, the implementation of the guidelines yielded positive results, as they were deemed suitable, feasible, and highly beneficial across all aspects.
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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 | 0.052 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".