Draw Me a Shepherd: Student-teachers' Perceptions and Metaphors on the Image of the "Leader-teacher"
Bibliographic record
Abstract
The current research examined how students participating in professional training in education and teaching characterized the concept of the "teacher leader". Layered analysis was employed using quantitative data alongside a qualitative examination of metaphorical representations indicating deeper unconscious perceptions. The significance of the investigation lies in revealing the meaning given in the student-teachers' reflective worldview to their future role as teachers and educational leaders. Participants were one hundred twenty-five undergraduate students studying in various stages of their B.Ed. courses in education and teaching at a teacher education college in Israel. The research process identified four key indices, describing the main qualities required for the teacher leader: Personal Relations, Student Empowerment, Personality Traits, and Functional Traits. Key findings indicated that, according to the students' opinions, Personal Attitude received the highest score of the four examined indices. Thus, from the perspective of these future teachers, a clear association was created between Personal Relations and Personality Traits, as the most significant dominant traits in the concept of the "teacher leader".
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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 | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".