Ecocritical Perspectives on the Healing of Nature: A Study of COVID-19 Poetry
Bibliographic record
Abstract
This research paper examines a curated selection of poetry written during the COVID-19 pandemic, highlighting nature's recovery from the trauma inflicted by humanity's relentless pursuit of industrialization and urbanization. The study explores literature's potential to inspire environmental awareness and foster constructive change. Since the Industrial Revolution, industrialization and urbanization have placed immense pressure on the natural world, leading to ecological imbalance, resource depletion, and climate change. This research aims to provide insights into how poetry engages with environmental themes, portrays associated challenges, and envisions pathways for rejuvenation. Situated within the framework of ecocriticism—a literary theory that investigates the relationship between literature and the environment—this study deciphers poems to uncover their environmental concerns, critiques of human behavior, and proposed solutions for healing. By employing ecocriticism as its primary methodology, the analysis reveals underlying ecological issues, incisive critiques of human actions, and thought-provoking solutions for rejuvenating our planet, as depicted in the selected COVID-19 poetry. The research draws upon a rich array of poetic works from the pandemic, focusing on those that capture the transformative power of nature's healing amidst the scars left by humanity's unyielding drive for industrialization and urbanization. It underscores the vital role of literature in nurturing environmental consciousness and catalyzing positive change. Grounded in the principles of ecocriticism, this paper offers insights into the intricate interplay between literature and the environment, presenting a unique perspective on the potential of poetry to inspire environmental awareness and prompt meaningful action for the healing of nature.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".