AN ANALYSIS OF EDUCATIONAL VALUE AND DEFENCE MECHANISM IN “LIFE OF PI” NOVEL BY YANN MARTEL
Bibliographic record
Abstract
This research was aimed to analyze an educational value and defence mechanism in life of pi novel by Yann Martel. Life of Pi is an award-winning novel written by Yann Martel, a Canadian author. The novel depicts the topic of struggle for life and against death in an emergency situation. It tells about the struggle of an Indian boy who spent 227 days with a fierce tiger in the Pacific Ocean, and as the sole survivor in a shipwreck that killed his family. There are two problems are formulated to guide and limit the discussion in this study. The first problem examines the description of the educational value, the second problem examines the defence mechanism in that novel. The methodology of this research is descriptive qualitative method. All of the data are gathered by reading the novel, identifying, classifying, and reducing the data. The primary data of the study are in the form of monologues and dialogues of the novel itself. The primary data is supported by secondary data that is taken from books, journals, articles, essays, and sites that relate to the study. The selected data is interpreted into understandable meaning by descriptive technique. The result of the research finds that educational value in life of pi novel: he is intelligent, he was able to enter the best secondary school in his town and later achieved top grades as a university student; open- minded, He is open-minded in his way of thinking; spiritual, He practices three religions with the reason that he wants to love God; and has strong determination, He has a strong will and he does not give up or become desperate easily. The defence mechanism carried out by Pi Patel: by recognizing and using his strength, which is made possible by his intelligence; by being realistic about the situation, which is facilitated by his open- mindedness; and by adopting positive attitude, which is facilitated by his spirituality and strong determination.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".