Quarter life crisis dan toxic relationship pada mahasiswa Studi Kasus: Mahasiswa Se- Jabodetabek Raya di Malang
Bibliographic record
Abstract
This quarter life crisis arises when students are faced with academic pressure, questions about life goals, and uncertainty about the future. Quarter life crisis can be an entry point for students to become involved in toxic relationships. The purpose of this research is to determine the relationship between the quarter life crisis and students who experience toxic relationships. This research uses a mixed method approach which combines quantitative and qualitative research. The population that is the subject of this research is Jabodetabek students who are currently studying in Malang. The sampling technique used was non-probability sampling which involved 133 respondents. The measurement instruments applied include the quarter life crisis and toxic relationship scales. The analytical method applied is simple regression analysis using IBM SPSS version 22 software for Windows. The findings of this research reveal that the majority of students who experience quarter life crises and toxic relationships are women. This is based on (p = 0.000 < 0.05). With a coefficient of determination (R Square) of 0.427, which means that the influence of the independent variable (quarter life crisis) on the dependent variable (toxic relationship) is 42.7%, while the remainder is influenced by other factors outside the variables of this research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".