A Study on Using the University Library and Reading Habits of Students: A Study on Tishk International University Students in Erbil, Iraq
Bibliographic record
Abstract
The library uses habits of the students and their choices what to read change dramatically from student to student. Reading habits of newspapers are indicated very low in statistics, while reading habits of books were relatively much higher. In addition, the habits of library use are different, so the underlying reasons should be delved into. The aim of this study is to reveal the use of the university library and reading habits of Tishk International University Education Faculty students. 200 students who were randomly chosen from 530 students from different departments of the Faculty participated in this survey voluntarily. Descriptive and correlational research model was used in the study. In the analysis of the data, the questionnaire technique and the “chi-square test” was used. According to the results of the study; students’ use of the university library and reading habits were found to be high (strong habits). On the other hand, it was concluded that students’ newspaper reading habits were lower than those of reading books. Students who think that they have not read enough books, magazines and similar reading sources have cited the problem of not finding time as a reason and the lack of the printed media and publishing in the country. In addition, it is clear that students with a high frequency of library use have academic success and a strong reading habit. On the other hand, no significant relationship was found between the frequency of reading the books and the levels of income and the level of education of parents.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".