Vision of Death in Emily Dickinson's Selected Poems
Bibliographic record
Abstract
Poetry is greatly influenced by the cultural background and personal experiences of the poets. Emily Dickson’s poems exemplify this because she draws a lot of her motivation from her heritage of New England and her life experience which had harsh incidents such as loss of friends and relatives. She lives a life of seclusion, where she rarely has face-to-face encounter with her friends as she prefers communicating through letters. Her limited interaction with the society gives her adequate space to reflect and write about different aspects of life. Emily’s poetry is also influenced by the doubts she holds about Christianity, especially in relation with survival of the soul after death. "Because I Could Not Stop for Death" and "I Heard a Fly Buzz- when I Died" are among her popular poems that indicate her religious doubt. She agrees with some of the Calvinist religious beliefs, but still has some doubts about the innate depravity of mankind and the concept of the afterlife.Dickinson’s spiritual background is indicated by her religious beliefs, which form the basis of her preoccupation with death. Although Dickinson is a religious person who believes in the inevitability of death and afterlife, she is a non-conformist as she is skeptical and curious about the nature of death. Transcendentalism is the other factor that contributes to Dickinson’s preoccupation with death as indicated in her poems. Dickinson’s preoccupation with death also results from her obsession, which is greatly contributed by the life experiences she has with death including loss of her family, mentors and close friends.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".