Self-Construal and Demographic Variables as Predictors of Blind and Constructive Patriotism in University Students
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".