Quarter Life Crisis “Aku Ga Bisa Yura”: Studi Fenomenologi pada Mahasiswa di Kota Semarang
Bibliographic record
Abstract
This research aims to examine the phenomenon of quarter life crisis among university students in Semarang City, which was triggered by the TikTok trend “Aku Ga Bisa Yura.” The scope of this paper includes an analysis of the five phases of quarter life crisis, starting from the feeling of being trapped, the desire to change the situation, to the crucial actions taken, as well as how individuals build a life according to their values and interests. The method used is a phenomenological study with in-depth interview techniques with five subjects representing various experiences and problems related to quarter life crisis. The results of the discussion show that students feel the complexity of facing the transition to adulthood, and although faced with uncertainty, they are able to take positive steps to achieve life goals that are more in line with themselves. The conclusion of this study confirms that quarter life crisis, although challenging, can be a process that results in significant personal development, and points to the need for further understanding of the influence of social media in shaping students' perceptions of this crisis. This research also suggests conducting a broader study involving students from different regions and other social media platforms.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".