VIDEO CHAMPAIGN: DECISION MAKING IN QUARTER LIFE CRISIS PHASE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of media technology is very rapid and with advances in technology that are increasingly developing day by day, the use of social media which can be accessed from mobile phones, the dominance of social media which is widely used, such as Instagram, is a favorite among Indonesian people, especially young people, with the number of users accessing it. Instagram is around 79% with various campaigns or interesting content. The campaign regarding the Quarter Life Crisis that has been created will be uploaded to Instagram with the aim of finding out how informative and interesting the campaign uploaded to Instagram is for teenage Instagram users. A quarter-life crisis is a crisis condition experienced in their 20s. Someone who is in Quarter Life Crisis often experiences emotional problems, where he is always confused about the various available options. The method used to create the campaign was data collection method through random interviews with FIK students and using the publication method via Instagram. The results obtained were seen through Instagram insights with a total audience of 497 users, 106 users liked, 35 users commented and 21 users shared.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it