Bibliographic record
Abstract
Colours can draw an identity to all living things. Natural colours can either calm down or disturb a person’s inner Self. At the times of crisis, it tends to give the individual soul wit and hope. Colours also have alchemical significance and can impact every man’s mind in certain ways. Colour representations have been used by many symbolists throughout literary history from the past to the present. Symbolists point out the importance of symbols in the poems of symbolist poetry. Moreover, the use of colour symbolism in literature contributes to the treasure of literary forms. In Canadian literature, Yann Martel holds a prominent position for his adaption of symbols and uses them to portray the inner quest of his characters. Frequently, his symbolism embodies a deep search for a spiritual quest with a religious component. Colour is one of the most important aspects in deciphering the psyche of his heroes. He has constructed various symbolic interpretations that exhibit the spiritual longing of individuals. Many colours like red, black, white, green, orange, etc have been used as symbolic representations to decode the mind set and religious beliefs. Among them, black and white colours play a vital role in an in-depth portrayal of the leading characters. The religious quest of the characters has been satisfied through the identification of colour representations and ancient relics. They were satisfied at the end. Hence, his works depict that colours have symbolic dramatic elements that naturally novelize the central theme of the search for Self. It also emphasizes the development of the Self with the supremacy of faith in the Almighty God. This paper deciphers the black and white colour symbols in the novels, “Self”, “Life of Pi”, “Beatrice and Virgil”, and “The High Mountains of Portugal” of Yann Martel.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".