PENGARUH LITERASI DIGITAL TERHADAP QUARTER LIFE CRISIS MAHASISWA ILMU PERPUSTAKAAN UIN SUNAN KALIJAGA
Bibliographic record
Abstract
This study aims to 1) determine the level of digital literacy of UIN Sunan Kalijaga \nLibrary Science students, 2) determine the level of quarter life crisis experienced \nby UIN Sunan Kalijaga Library Science students, and 3) determine the effect of \ndigital literacy skills on quarter life crisis of UIN Sunan Kalijaga Library Science \nstudents. This research uses quantitative methods with descriptive and correlation \ntypes. The subjects are Library Science students class of 2019 and 2020, and the \nobject is the influence of digital literacy on quarter life crisis. Sampling using \nproportionate stratified random sampling technique with a sample gain of 69 \nstudents. Data collection was carried out using observation, interview, \ndocumentation, and questionnaires. Data were analyzed using mean, grand mean, \nproduct moment correlation, and simple linear regression. The results of the \nanalysis show that 1) students' digital literacy is in a very high category with a \nvalue of 3.4, 2) students experience a quarter life crisis of 2.85 in the high \ncategory, and 3) digital literacy affects quarter life crisis with a significance value \nof 0.000 <0.05. Based on the coefficient of determination, digital literacy has an \neffect of 18.4% on quarter life crisis. Product moment correlation analysis shows \na pearson correlation value of -0.429, which means the higher the digital literacy, \nthe lower the quarter life crisis, and vice versa.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".