Examining The Quarter-Life Crisis of Library Science Students at UIN Sunan Kalijaga Yogyakarta Through Digital Literacy Impact
Bibliographic record
Abstract
Rapid digital advancements present opportunities and challenges that significantly affect young adults' mental and emotional well-being. This study aims to 1) determine the level of digital literacy of UIN Sunan Kalijaga Library Science students, 2) determine the level of quarter-life crisis experienced by UIN Sunan Kalijaga Library Science students, and 3) determine the effect of digital literacy skills on quarter life crisis of UIN Sunan Kalijaga Library Science students. This research uses quantitative methods with descriptive and correlation types. The subjects are Library Science students’ classes of 2019 and 2020, and the object is the influence of digital literacy on the quarter-life crisis. Data collection was carried out using interview techniques, documentation, and questionnaires. Data were analyzed using mean, grand mean, product-moment correlation, and simple linear regression. The results of the analysis show that 1) students' digital literacy is in a very high category with a value of 3.4, 2) students experience a quarter-life crisis of 2.85 in the high category, and 3) digital literacy affects quarter-life crisis with a significance value of 0.000 <0.05. Based on the coefficient of determination, digital literacy affects 18.4% of quarter-life crises. Product moment correlation analysis shows a Pearson correlation value of -0.429, which means the higher the digital literacy, the lower the quarter-life crisis, and vice versa.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".