PENGARUH DUKUNGAN SOSIAL DAN KEMATANGAN KARIR \nTERHADAP QUARTER LIFE CRISIS PADA MAHASISWA \nTINGKAT AKHIR
Bibliographic record
Abstract
Quarter life crisis is an emotional crisis caused by unstable conditions during the transition of adolescence to early adulthood that encompasses doubts, worries, and fears \nrelated to the future. Individuals are generally studying in college and have student status. \nSocial support becomes one of the external factors and career maturity becomes the internal \nfactor that helps final students face the quarter life crisis. The aim of this study is to find out \nthe impact of social support and career maturity both partially and simultaneously on the \nquarter life crisis in final students. The research uses quantitative survey methods with double \nlinear regression analysis. A total of 350 subjects using accidental sampling. The subject \ncriteria are undergraduate, minimum semester 8, and age from 18 to 29. Social support is measured with the MSPSS (Multidimensional Scale of Perceived Social Support) that has been \nadapted. Career performance is measured with the CMI (Career Maturity Scale) which has \nbeen adapted. Analysis shows that there is a significant negatif influence between social support \nand partial and simultaneous career maturity on the quarter life crisis in final students. \nSimultaneously, the influence of both independent variabels on the quarter life crisis is 60.8% \nand the rest is influenced by other factors that have not been studied
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".