The Metaphorical Perceptions of Teacher Candidates Attending the Pedagogical Formation Program on Academic Staff—Gazi University Sample
Bibliographic record
Abstract
Teacher training in Turkey has a long history with various practices. It has taken a different dimension with training teachers through pedagogical formation program certificates that last for a short time. The aim of this research is to reveal the metaphors of teacher candidates attending pedagogical formation program towards the academic staff. The research was designed with qualitative model and phenomenology design was used. The sample of the study group was composed of 392 teacher candidates. Teacher candidates were asked to fill in the sentence; “Academic staff during the pedagogical formation program is like ….….; because …………………”. The data analysis was conducted with qualitative methods. The academic staff is described in 11 basic categories. Most of these categories are made of positive behaviors composing 86.47% as guiding, advisor and counselor, the resource of knowledge and experience, constructive and developer, multi-perspective, self-developing, open to change, respectful, patient, tolerant and democratic, role model, using effective body language and presentation techniques. The remaining 4 categories are made of metaphors presenting the negative behaviors of academic staff reflecting 14.03% of teacher candidates: just narrating, not communicating enough, authoritative and oppressive, behaving inconsistently and irresponsive, non-communicative and with high ego.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".