Discovering the Relationships Between Perceived Emotional Value, City and Friend Attachment, School ID, WOM and Educational Satisfaction: A Structural Research for University Students
Bibliographic record
Abstract
Rapid transformations in higher education in recent years have made deeper researches about student-university relationship mandatory. Regarding the subject, Anderson, Sullivan, and Swidler (1993) stated that students who are the consumers of universities will have very important benefits for higher education institutions in determining the factors related to education satisfaction. So, the aim of this research is designed to determine the relationships between university students’ satisfaction with education, the emotional value they perceive, school identity, city attachment and positive WOM behavior. For the relevant purpose, 366 university students were reached online. The empirical data obtained were analyzed with a two-step approach (measurement and structural) in line with the basic methodological principles of SEM. Acceptance of all hypotheses created within the scope of the structural model proved empirical relationships between the related structures. The research has provided new perspectives to the ongoing discussions in the related literature by proving the direct or indirect effects of structures such as city attachment, emotional value, and school identity on university satisfaction. The results provided tips for professionals in the field to create educational satisfaction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".