THE RELATIONSHIP BETWEEN FEAR OF MISSING OUT AND QUARTER LIFE CRISIS WITH PROCRASTINATION IN PRIVATE UNIVERSITY STUDENTS IN SIDOARJO
Bibliographic record
Abstract
Objective: Education is long life or all the time. Education in Indonesian is divided into several levels from elementary, secondary, to tertiary levels. In Indonesia, education is under the Ministry of Education, Culture, Research and Technology. College is a stage for someone who wants to earn a bachelor’s degree. However, some students who have entered the final semester still have not completed their studies. Of course, there are things behind the student. Procrastination is something that students experience. From this, procrastination becomes the focus of this research. Method: The research employs a quantitative method with an axial sampling technique. The population in this study is all students of private universities in Sidoarjo, and the analysis technique uses Pearson correlation. Results: The result of β = 0.473 variables of fear of missing out (X1) and β = 0.649 variables of quarter life crisis against procrastination were obtained, indicating a positive relationship between variables X1 and X2 to variable Y. Novelty: This research provides new insights by examining the influence of fear of missing out and quarter life crisis on procrastination among final semester students in private universities, a focus that has not been extensively explored in previous studies.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".