PERKEMBANGAN KEJIWAAN MUSA DALAM NOVEL MADIELIEF KARYA KIRANADA: KAJIAN TEORI PSIKODINAMIKA SIGMUND FREUD
Bibliographic record
Abstract
Novels are never separated from psychological values because the problems told in novels are always related to the psychology of the characters created by the author. This psychological aspect is usually displayed through the character traits and behavior of the characters that are a problem as experienced by humans in real life. Like the novel Madielief by Kiranada which tells the psychological problems of the characters in it. Therefore, this research is examined using psychodynamic theory which aims to describe the psychological development of characters according to Sigmund Freud. This type of research is qualitative research with a literary psychology approach. The data were obtained from the novel Madielief by Kiranada. Research data are in the form of words, phrases, and sentences in paragraphs related to the problem statement. Data collection techniques using reading and note techniques. The results of this study indicate 1) Musa’s personality background in Kiranada’s Madielief novel, 2) Musa’s neurotic form of anxiety in Kiranada’s Madielief novel, 3) The psychological development of Musa in the Canadian Madielief novel
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 | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".