Bibliographic record
Abstract
The research attempted to answer the question: “What do Philippine teachers perceive as important traits and behaviors of good and bad leaders?” Related to this were three sub questions:1. How do Philippine teachers compare with those in other countries in their perceptions on leadership?2. Do male and female Philippine teachers share similar perceptions on leadership?3. Do old and young Philippine teachers share similar perceptions on leadership?A questionnaire asked 90 Filipino teachers to rank their top three choices from among 8 traits of good leaders, then among 8 behaviors of good leaders, then 8 traits of bad leaders, and finally 8 behaviors of bad leaders. Comparisons were then drawn between the Philippine results and those in other countries, as well as between males and females within the Philippine sample, and younger and older Philippine teachers.Philippine teachers clearly valued honesty as the most important trait, and showing respect as the most important behavior of a good leader. This result is slightly different from that of some other countries, where, for example, intelligence or dependability was deemed the most important trait.Further, the study revealed several significant differences on several items between men and women, as well as between old teachers and young teachers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".