The Acquaintance Level of Turkish Prospective Teachers with Qualified Works of Children’s Literature
Bibliographic record
Abstract
The aim of this cross-sectional study is investigate to what extent acquainted prospective Turkish teachers are with qualified works of children’s literature. A convenience sample of 146 university students studying at the Turkish teaching department at a university in the Central Black Sea Region completed a questionnaire to determine the qualified works of children’s literature about which the students knew. Firstly, books which were assessed as quality by students for primary and secondary school pupils were classified using frequencies and percentages. In the second stage of the analysis, appropriate books suiting the definition of quality books for children were selected according to Çer’s (2016a, 2016b) suggestions, and the foreknowledge levels of each student for quality books were determined. The Mann-Whitney U test and the Kruskal-Wallis H test were performed to examine gender and grade level differences in foreknowledge levels. Results of this study showed that the level of foreknowledge of Turkish prospective teachers about quality books for children for primary and secondary school students was quite low. Additionally, being female and grade level is associated with qualified works of children’s literature which the students knew. Such that, female prospective teachers significantly more acquainted with quality books for secondary school than males and the junior and senior students were significantly more acquainted with books for primary school than the freshmen and sophomore students. Senior students were also significantly more acquainted with books for secondary school than the freshmen, sophomore and junior students.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".