Bibliographic record
Abstract
The credibility of educational leadership is very important and even the life of a leader. Leaders who have credibility, then subordinates work sincerely, loyally and have a high sense of belonging to the organization. In addition, what is more important is the positive implication on the performance of educators and education personnel and on the quality of graduates. Conversely, if an educational leader has low credibility, then subordinates will work carelessly, work if they are rewarded and supervised and the sense of belonging to the organization is very low or even non-existent. And what's worse is the impact on the decline in the performance of subordinates and the low quality of graduates. This study uses a descriptive qualitative approach. The results showed that the credibility of educational leadership in general was good. Where educational leadership has implemented what is an indicator of credibility, which has implemented a good attitude of honesty, fairness, responsibility and the ability to inspire subordinates. However, there are still things that have not been implemented optimally, namely those related to interactional and distributive justice in terms of unequal reward and intensity that is still lacking and the ability to inspire in the motivating aspect has not used symbols in educational institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.018 |
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".