Perceptions of Primary School Teacher Candidates towards the Turkish Education System, School, Teacher, and Student: A Metaphor Analysis
Bibliographic record
Abstract
This study aimed to determine the perceptions of primary school teacher candidates about the Turkish education system, school, teacher, and student concepts by means of metaphors. The study group consisted of 82 primary school teacher candidates enrolled in the senior class of a university in the Black Sea Region in Turkey in 2018. The study data were collected using a questionnaire which involved gap filling questions aiming to determine the metaphors for the Turkish education system, school, teacher, and student. Findings indicated that the majority of the primary school teacher candidates had a negative perception of the Turkish education system. More than half of the negative metaphors that the participants used were about the unceasing change of the system. Primary school teacher candidates' perceptions of the school concept were mostly positive. The participants saw school as a home that educates and shapes people. Nevertheless, a considerable number of the participants considered school like an oppressive and uniformizing prison, where they would not like to be. Majority of the primary school teacher candidates had positive perceptions of the teacher concept. Nonetheless, there were neutral and negative perceptions as well. The participants mostly emphasized the educating and shaping characteristics of the teacher concept in their descriptions. Although primary school teacher candidates’ perceptions of student were generally positive, a student description, in which student was seen passive in the learning process and highlighted as an entity that can be shaped, stood out.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".