Historicity of Ecocriticism and Ecocritical History: An Introductory Overview
Bibliographic record
Abstract
Overlapping and interconnected, interdisciplinary and heterogeneous, amorphous and multi-layered, and deep and broad as it is, countless topics on ecoliterature make ecocriticism a comprehensive catchall term that proposes to look at a text--be it social, cultural, political, religious, or scientific--from naturalist perspectives and moves us from “the community of literature to the larger biospheric community which […] we belong to even as we are destroying it” (William Rueckert). As I was in the middle of writing and researching for this article, I was struck by a piece of nature writing by an eleven year old sixth grader born to his (South Asian and American) mixed parents, both affiliated with Johns Hopkins and already proud to belong to the extended family of a Nobel Laureate in Physics. The young boy, Rizwan Thorne-Lyman, wrote, as his science story project, an incredibly beautiful essay, “A Day in the Life of the Amazon Rainforest.” Reading about the rainforest was one of his interests, I was told. In describing the day-long activities of birds and animals among the tall trees and small plants, the 2 pp.-long narrative actually captures the eternally continuing natural cycle of the Amazon. The budding naturalist’s neat classification of the wild life into producers (leafy fruit and flowering plants and trees), consumers (caimans/crocodiles, leafcutter ants, capuchin monkey), predators (macaws, harpy eagles, jaguars, green anaconda), decomposers (worms, fungi and bacteria), parasites (phorid flies) and scavengers (millipedes) was found to be unforgettably impressive. Also the organization of the essay into the Amazon’s mutually benefitting and organically functioning flora and fauna during the day--sunrise, midday, and sunset--was unmistakably striking. I congratulated him as an aspiring environmentalist specializing in rain forest. I encouraged him that he should try to get his essay published in a popular magazine like Reader’s Digest (published did he get in no time indeed![i]) and that he should also read about (and visit) Borneo in Southeast Asia, home to other great biodiverse rainforests of the world. I called him “soft names” as a future Greenpeace and Environmental Protection leader and theorist, a soon-to-be close friend of Al Gore’s. The promising boy’s understanding, however short, of the Amazon ecology and ecosystem and the biological phenomena of its living organisms was really amazing. His essay reminded me of other famous nature writings, especially those by Fiona Macleod (see below), that are the pleasure of those interested in the ecocriticism of the literature of place--dooryards, backyards, outdoors, open fields, parks and farms, fields and pastures, and different kinds of other wildernesses. [i] https://stonesoup.com/post/a-day-in-the-life-in-the-amazon-rainforest/
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".