Self-Construal and Demographic Variables as Predictors of Blind and Constructive Patriotism in University Students
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this study is to investigate the blind and constructive patriotism trends of university students in light of the demographic structure and variables. The investigation is performed by using the correlational descriptive model. The purposeful sampling technique is used and data was collected from 390 university students. 225(%57.7) of the participants are female, and 65(%42.3) are male, the age ranges vary between 18 and 26 and the mean of the age is 20.42(SD=1.88). Demographic Information Form, Patriotism Attitude Scale and Relational, Individual and Collective Self Aspects Scale has been applied to the participants. The correlation, t-test, analysis of variance and regression analysis techniques were used in the analysis of the data. The obtained results reveal that, the blind patriotism scores of the participants show a significant difference according to sex. It was found that, the blind patriotism scores show differences according to the city they live in. On the other hand, it should be noted that there is a relationship between the blind patriotism and relational aspect and collective aspect of the self. Also it has been seen that there is a significant relationship between the constructive patriotism and the relational aspect, individual aspect and collective aspect of the self. Finally, it was found that the collective self-aspect, being a man and continue to the education in Sivas are meaningful predictors of the blind patriotism; the collective self aspect is a significant predictor of constructive patriotism.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it