Ideologies and linguistic policies – an analysis on the literary work Amik loves school: a story of wisdom
Bibliographic record
Abstract
We use the literary work Amik loves school: a story of wisdom (VERMETTE, 2015a) in this article as a motivator to reflect upon language ideologies (WOOLARD, 1998; KROSKRITY, 2004; BAKHTIN, 2009), colonization (HELLER; MCELHINNY, 2017), erasure (IRVINE; GAL, 2000) and, thus, problematize some Canadian linguistic policies, such as the Indian Act (1876). According to the document in question, the indigenous education had to follow the standards adopted by white European linguistic and cultural identities, which strengthened imperialistic ideologies that delegitimized the languages and cultures of Canadian first nations in contexts such as the residential schools. However, Katherena Vermette reveals that her literary work is designed with the aim of promoting ruptures with the assimilation processes in question, which intend to engage her readers not only with indigenous characters and their stories, but, above all, with reflections able to both deconstruct imperialistic ideologies and value the linguistic and cultural identities of these indigenous peoples. Vermette’s text reminds us of the trajectory faced by Brazilian indigenous peoples when subdued by colonization and their current subaltern condition. This way, historical and sociocultural similarities shared by both Canadian and Brazilian ethnic groups lead us to believe that the reading and the discussion of Vermette’s work in English teaching-learning contexts in Brazil can promote dialogues between different languages and cultures and their value by students.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.041 | 0.076 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| 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".