Disrupting the Discourse Behind the Zoning of the Native Americans: Linda Hogan’s Mean Spirit in the Environmental Racist Perspective
Bibliographic record
Abstract
This paper disrupts the Euro Americans’ environmental colonialist discourse which involves the practices of racist policies that result in the relocation of the Native Americans to a confinement called reservation. More specifically, it discusses this relocation which is termed as zoning as a dilemma for the Natives because this practice by the Euro Americans, which is primarily involves their economic agenda, not only restricts the Natives to their reservation and denies life opportunities for them but puts the responsibility of their plight on themselves. A qualitative content analysis, the research explores this idea in Linda Hogan’s Mean Spirit in the light of the joint critique of environmental racism and critical discourse analysis. Linguistically, the study applies critical discourse analysis focusing on van Dijk’s concept of discourse and manipulation. The analysis reveals that the discursive and cognitive strategies employed by the Euro Americans for the zoning of the Natives help the former rationalize and legitimize their environmental colonial practices. The discursive process first involves the creation of “othering” and then the tactful presentation of this “othering”. The study also highlights the counter actions taken by the Natives on the basis of the same or similar strategies as have been employed against them, to resist their zoning.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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 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".