Turkish Secondary Education Students’ Perceptions of Justice and Their Experiences of Unjustice
Bibliographic record
Abstract
The purpose of this study was to identify how secondary education students define the concept of justice, based on which criteria they define their experiences as just/unjust, what they see as the source of injustice, how they feel and how they behave when they face injustice.This study was designed as a qualitative research study. Open-ended questions were asked to the students and they were asked to give detailed answers. Descriptive analysis was used in analysing the collected data. The study group consisted of students studying at 9th, 10th, 11th and 12th grades in different types of high schools (Anatolian High School, Vocational School for Girls, Science High School). A total number of 268 high school students participated in the study. We used convenience sampling to choose the study group.The results of the study indicated that students defined the concept of justice with the “equality, equity non-discrimination, respect, rights and freedoms, conscience, rights, deciding the right, being fair and needs” concepts. The students see grades, school rules and non-communication they experience with their teachers and principals as unjust, so they point the school principals as the source of injustice. Besides, the students indicated that they had negative feelings when they experienced something that is not just but preferred to stay silent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".