School Heads’ Core Behavioral and Leadership Competencies: Basis for Action Plan
Bibliographic record
Abstract
This present study attempted to seek and assess the school heads core behavioral and leadership competencies in relation to their office performance commitment and review form. This study was conducted in the 3rd quarter to School Year 2024-2025. This research employed the descriptive correlation research design and used a standardized questionnaire. The respondents were the school heads of Iligan City Division. The results showcased a diverse range of age groups. This distribution underscored the participation of a broad spectrum of age demographics. An overwhelming majority were identified as female. The educational attainment distribution illustrated a relatively balanced distribution across different levels. Also, the finding indicated a predominant representation of School Principal designation.The findings summarized the core behavioral competencies by key areas and the corresponding level based on mean values. This study revealed that there was a significant correlation between the performance rating of school heads and their core behavioral competencies. It showed that the performance rating of school heads was moderately strongly correlated with self- management but was slightly weak with the other areas. On the other hand, the relationship between respondents' leadership competencies and their Office Performance Commitment and Review Form (OPCRF) ratings indicated that other factors not included in the model may play a significant role in determining performance outcomes.This result suggested that higher engagement in managing people’s performance might be associated with lower performance ratings. An action plan was crafted in cognizant of the influence that one's competencies and skills be connected to his/her performance.
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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 | 0.004 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".