Relationship between Individual Social Responsibilities and Personal Values of Teacher Candidates
Bibliographic record
Abstract
The aim of this study is to reveal the relationship between individual social responsibilities and personal values of primary school and music teacher candidates on the basis of gender, school grade and department variables. Survey model among quantitative methods was used in the research. The research sample consist of 162 (69.8%) primary school teacher candidates and 70 (30.2%) music teacher candidates receiving education in Adnan Menderes University Faculty of Education. 146 (62.9%) of the participants are female, and 86 (37.1%) were male teacher candidates. Also 104 (44.8%) freshman and 128 (55.2%) senior students were joined into the research. Questionnaire on Individual Social Responsibility and Questionnaire on Personal values were used as data collection tools. SPSS 21.00 statistics software was used for data analysis. Due to the normal distribution of data, t-test and one-way analysis of variance were conducted, then Pearson correlation coefficients were calculated and regression analysis was performed. According to the obtained findings, personal values total scores of the participating teacher candidates did not significantly differ based on their gender and department, whereas they significantly differed in a school-grade based evaluation. The individual social responsibility total scores of the teacher candidates did not show a statistically significant difference on the basis of gender. Individual social responsibility levels were found to differ significantly based on school grade and department variables. Statistically significant positive and low level (between .18 and .39) correlations were found between the teacher candidates’ social responsibilities and academic average and personal values subscales.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".