Indicators and Approaches for Developing Leadership of School Administrators in the New Normal Era in Schools Under the Primary Educational Service Area Office in the Northeast
Bibliographic record
Abstract
Research on developing leadership indicators and approaches is crucial for school administrators in the new normal era. It helps them adapt to rapid changes, integrate digital technologies for effective management, and foster educational resilience amid uncertainties. Strong leadership ensures schools remain innovative, stable, and future-ready. This study aimed to develop indicators and approaches for enhancing the leadership of school administrators in the new normal era, under the Primary Educational Service Area Office in northeastern region. The research employed a mixed-methods research, divided into four phases: phase 1 was reviewing relevant literature, academic documents, and research studies to identify key components and indicators; phase 2 was validating the model against empirical data through a questionnaire survey conducted among sample of 400 school administrators under the Primary Educational Service Area Office in northeastern region; phase 3 was developing leadership development approaches based on in-depth interviews with five experts; and phase 4 was assessing the proposed approaches through a qualitative focus group discussion with nine specialists. The findings revealed that: (1) the 50 indicators identified in the study meet the established criteria for appropriateness; (2) the developed model was consistent with empirical data; (3) the leadership development approaches in the new normal era consisted of five key components, ranked by their respective factor loading as follows: digital technology competency, organizational communication, creativity, visionary leadership, and social and legal ethics; and (4) all indicators demonstrated appropriateness, feasibility, usefulness, and comprehensive validity.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".