Decolonizing English: a proposal for implementing alternative ways of knowing and being in education
Bibliographic record
Abstract
There is a need to decolonize English in order to reframe our relationships with fellow beings and our environment. English can frame water or oil as infinite, uncountable nouns , a tree as an inanimate, unconscious being, traditional and respected territories as wasteland, and animals as wildlife. With the current climate crisis, we know that these categorizations fall short and can normalize environmental racism and injustice. A more equitable and sustainable way to use language would be to question the worldview or belief system that informs “ecologically destructive” assumptions and perceptions. The English language also carries a colonial and assimilationist legacy. In many cases, this colonial history is omitted in our history books or plainly avoided in many forms of curriculum. The danger of ignoring this legacy resides in the human exceptionalism, or “epistemological error”, which dominates the current mainstream Western worldview, colonial education, and the English language. This paper proposes decolonizing the English language and exemplifies how we can do this and why we should learn from and implement ecocentric worldviews, such as those which are Indigenous.
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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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.068 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".