Career Decision Making and the Quarter-Life Crisis in Generation Z
Bibliographic record
Abstract
People in their 20s are surely no strangers to the term Quarter Life Crisis, which becomes the most difficult period in life. This age usually has many opportunities and responsibilities to explore careers and personal lives. Sometimes a person can still choose what is more suitable for themselves, even if it means going against other choices. The doubts that arise at this age are varied, ranging from self-doubt, adaptation, skills, abilities, and much more that are inherent to a person, which is indeed no longer an easy matter. The multitude of inner turmoil will make someone dizzy, stressed, and even decide to choose not to choose anything at all. The purpose of this research is to determine whether there is a relationship between career decision-making and Quarter Life Crisis in Generation Z. This research uses a correlational quantitative approach. The subjects of this research are individuals from Generation Z, specifically those aged 18 to 21 years, who are currently enrolled in the Guidance and Counseling (BK) Department, Faculty of Teacher Training and Education (FKIP), class of 2023, University of Borneo Tarakan (UBT). A total of 54 students. The data analysis technique used is the Pearson Product Moment correlation with the Jamovi version 2.3.28 software. The results of the Product Moment (Pearson) correlation test obtained a correlation coefficient (r) value of 0.318 with a significance value of 0.000 < 0.05, indicating a positive and significant relationship between career decision-making and Quarter Life Crisis. This means that the higher the level of emotional maturity of a person, the higher the Quarter Life Crisis they will experience, and vice versa. The relationship between the two variables is weak.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".