Death as Transformation: Examining Grief Under the Perspective of the Kubler-Ross in the Selected Movies
Bibliographic record
Abstract
Death has always been a central human concern. Death is transformative; for those left, therefore, the experience of grief and loss opens another world. The meaning of grief is not simply the “Loss of …” but the “Intense sorrow caused by the loss of a loved one (especially by death)”. Grief is the price we pay for love. The deeper the love, the greater the depth of the grief that follows the loss. Grief is a shape of emotional pain; however, human beings no longer constantly trip these levels in any unique order, nor do they trip each stage. This paper draws upon the conceptual framework of Kubler-Ross five stages of grief to analyze the following movies “UP”, “Baba Dook”, “The Kite Runner”, “Rabbit Hole”, “Summer 1993” and “Three Colors: Blue” content analysis as the method of analysis. Besides, this paper explores the impact of these five stages of grief on different genders through the characters and scenes in the selected movies. This paper is an exploratory and descriptive study grounded in qualitative research design and uses content analysis as the method of analysis of the selected movies. The findings of this study show that death is a transformative phenomenon and grief unlike other emotions is a powerful tool since it raises doubt about how the grieved discovers significance throughout everyday life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".