Earth as Mother, Environment as Right: Tracing the Conceptual Foundations of Ecocide Across Time
Bibliographic record
Abstract
The article presents an extensive inquiry into humanity’s evolving ethical, cultural and legal engagement with the natural environment, advancing the claim that large-scale ecological destruction must be conceptualised as a grave offence against human civilisation. Drawing upon ancient Indian intellectual traditions, including Vedic hymns, the epics Ramayana and Mahabharata, as well as Kautilya’s Arthashastra, the study illustrates that nature was historically perceived not merely as a resource base but as a sacred, life-sustaining entity. These texts consistently conveyed an ecocentric worldview that regarded harm to forests, rivers, flora and fauna as an existential threat to human survival an understanding paralleled in Indigenous cosmologies such as those of Native American communities. Against this long-standing moral background, the article explores the contemporary juridical vocabulary through which environmental harm is understood. The Supreme Court of India’s use of the term matricide to describe destruction of ecological systems underscores the symbolic framing of Earth as a mother figure whose violation constitutes a moral transgression. The discussion then traces the emergence of ecocide as a proposed international crime, beginning with Arthur Galston’s formulation in 1970 and strengthened by later political and academic developments including the ENMOD Convention, Additional Protocol I to the Geneva Conventions, and the Rome Statute. The 2021 definition crafted by the Independent Expert Panel characterising ecocide as unlawful or wanton acts causing severe, widespread or long-term environmental damage marks a decisive step toward global criminalisation, a trend now reflected in several national jurisdictions and in recent observations of the Indian Supreme Court. The article further situates ecocriticism within this discourse by assessing how literary traditions, from Wordsworth, Keats and Eliot to Kalidasa, Tagore and contemporary Indian writers, have articulated humanity’s dependence on ecological stability. Their works collectively warn against excessive anthropogenic intrusion. Bringing these trajectories together, the article argues that recognising ecocide as a crime against humanity is both a legal necessity and a civilisational imperative.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.123 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| 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".