PODCASTS UTILIZATION THROUGH INSTAGRAM MEDIA IN INCREASING THE MOTIVATION OF THE MILLENNIAL GENERATION IN THE QUARTER LIFE CRISIS PHASE
Bibliographic record
Abstract
Quarter life crisis is a feeling that arises when an individual reaches a quarter of a century (towards 25 years), where there is a feeling of fear about the continuation of life in the future, including career matters, relationships and social life. A person in this crisis experiences a loss of motivation to live, feels like a failure, loses self-confidence and meaning in life, and even withdraws from social interactions. Delivering motivation to the millennial generation through podcast media aims to find out how behavior changes and ways to adapt in phases quarter life crisis. The research method used is a descriptive qualitative approach, which starts with data collection through interviews with sources via an application chat audio Whatsapp on October 11 2023, then uploaded the podcast on the Instagram page and conducted a survey of podcast listeners through likes and comments on the Instagram post where the podcast was uploaded. Through this research, the results were obtained from a podcast with the title “How To Deal With Quarter Life Crisis?” This has received a lot of attention from Instagram users and can be a solution to increase the motivation of the millennial generation in facing this phase quarter life crisis
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".