The Extent of Applying Citizenship Values Among Jordan Universities Students
Bibliographic record
Abstract
The study aimed at recognizing the degree of the teaching staff members in Irbid National University employment of citizenship values in teaching from the point of view of students, and also specify if there are differences with statistical significance ascribed to the variables of the study, they are: sex (male & female) and type of the faculty (human and scientific). To achieve the study's objectives, both researchers applied the descriptive-analytical method, employing a questionnaire prepared by both researchers. The instrument of study covered (21) items, they had been distributed after assuring procedures of validity and reliability on a class on a random class sample, its component is (512) male and female students from the community of study amounting to (2592) during the second semester of the studying year (2017-2018). The most prominent results were that the teaching staff member's citizenship values came at a medium degree through the teaching process. Also, differences with statistical significance did not appear ascribed to both variables of sex (male & female) and the faculty (human & scientific). And in light of results, the researchers recommend the University Administration entrust the subject of citizenship greater importance and bid the professors to evaluate those values and enhance them in cultural and academic domains and activities, through the different plans of the studying subjects.
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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 | 0.004 |
| 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 | 0.002 | 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".