SELF-ACTUALIZATION APPLICATION IN HELPING HUMAN CHARACTER DEVELOPMENT BASED ON FLORENCE LITTAUER PERSONALITY TEST BASED ON ANDROID
Bibliographic record
Abstract
A midlife crisis is a stage when a person feels anxious, worried, and confused about their purpose in life. This condition occurs when a person currently doubts their future and quality of life. The quarter-life crisis phase can be overcome with better self-awareness, better self-awareness and appreciation, and increased user productivity. Therefore, self-realization is the right solution to survive the Quarter Life crisis. Self-actualization (selftuazion) offers several interesting features tailored to the needs of users in self-actualization, such as self-assessment, ToDo application and statistics, interesting articles, self-introduction pages, motivational quotes, and reminder notifications to help in the process of loving yourself. The results of the pilot test on 22 target group respondents showed that the functions contained in Selftuazion had a good effect on respondents in overcoming the problem of Quarter Life Crisis. This application produces 20 pages with features that are in accordance with self-actualization, based on the results of testing with users that the application can be used properly and get a score of 88 in the acceptable category with grade B or Good.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.016 | 0.004 |
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".