Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it