Quarter-Life Crisis: Dealing with a Reality that Doesn't Align with Expectations and Aspirations
Bibliographic record
Abstract
Quarter life crisis occurs when a person reaches the age of 18 to 29 years. A person will begin to find it difficult to face the world, find it difficult to control their emotions, and begin to question whether the life they are living is the right life. So there is a need for more information about the quarter life crisis. One solution that can be considered is to provide information and disseminate it so that individuals recognize and understand how to respond to the quarter life crisis by using posters. The research method used in this research is qualitative with a survey of Instagram posts with the characteristics of respondents, namely people aged 20 years and over who have Instagram and see posts that researchers have posted on the Instagram page. The instrument used was in the form of an Instagram post containing the meaning, characteristics and factors causing the quarter life crisis which were then analyzed using descriptive analysis. The results of this research show that the majority of respondents are experiencing a quarter life crisis. It is hoped that with this post about the quarter life crisis, they will be able to realize that they are experiencing a quarter life crisis and can face the appropriate measures
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".