Bibliographic record
Abstract
This research article very critically scrutinizes how forests in North America are devastated by the growing human civilization. It deals with ecological degradation in an American novelist Annie Proulx’s novel Barkskins whose location is North America. In course of analysing the novel critically, the article describes how Barkskins revolves round the story of white colonists and indigenous Indians in North America or today’s Canada. Firstly, it reveals how two families: Sel family (a poor biracial family of French and Mi’kmaq) that cuts trees and Duke family (rich French family) that does business of fur are linked to trees and deforestation. Secondly, the article focuses on the impact of western civilization on forests regarding forests as the antagonist to western civilization. Western colonialism is also a vehicle of civilization that causes deforestation. Due to civilization, humanism is developed. So, anthropocentric nature of people causes deforestation. Thirdly, European civilization has a negative impact on Indigenous people and their culture. Apparently, forests are shown as a symbol of darkness, evil forces, backwardness and an obstacle for human progress, but in the name of civilization, whites do deforestation due to their greed of colonization and anthropocentric nature. Hence, the first objective of the research is to explore why the whites regard forests as the antagonist to civilization. Likewise, the second objective of the article is to discover the real cause of them to do deforestation. Besides, as for the broad theoretical methodology, Greg Garrard’s theory of Ecocriticism is applied for the textual analysis of Barkskins since the article deals with the ecological destruction of North America by whites and ecocriticism has emerged as a response to the heavy damage done to ecology by human beings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".