Transforming Values into Behaviors: A Study on the Application of Values Education to Families in Turkey
Bibliographic record
Abstract
No matter what century we live in, even though the tools we use change from age to age, man is not a creature who can be considered or understood without the concept of values. Although we have different religions, languages, races and cultures, the personality of man is always constructed through values. Values are factors that directly influences human life and society in a positive or negative way. This study suggests that values education aimed at teaching individuals certain values is not sufficiently practiced by families in Turkey. In order to address the problem, this study aimed to increase the awareness of family members regarding values and help them turn values into behavior in everyday life. To this end, a 24-month “values education program” involving a set of activities was carried out. Every month, a specific value was chosen taking into account the needs of family members and “value booklets” were prepared using four sub-dimensions of the chosen value. 10 families participated in the program and the data was collected from 25 individuals. The resulting data was subjected to content analysis. 3 main themes were found to be important in the light of the data: moral development, development of communication skills, and religiousness. These themes were thought to be beneficial in terms of understanding the effectiveness and importance of family members’ internalizing values and turning them into behavior in everyday life.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".