Dialect speakers, academic achievement, and power : First Nations and Métis children in standard English classrooms in Saskatchewan
Bibliographic record
Abstract
This doctoral dissertation focuses on the negotiation of power in schools and the social and academic experiences of First Nations and Metis children who speak a non-standard variety of English called Indigenous English. Indigenous English is a dialect of English spoken by many Indigenous peoples in Canada; it is especially discernable in the Prairie Provinces, yet it is not widely recognized by the majority of the population. This thesis explores the experience of dialect speakers of Indigenous English in the standard English School and educator perceptions of their literacy and language abilities. This classroom study was conducted in an urban community in Saskatchewan. The focus of the research was a Grade 3/4 classroom with 25 students, six of whom were interviewed for this study. Additionally, interviews were conducted with eleven educators. The results of this study indicate that the First Nations children of this study speak a dialect of English that differs phonologically, morphologically, syntactically, and lexically from the Standard English spoken in Saskatchewan. The results of this PhD research indicate that Indigenous English-speaking students use discourse behaviour that differs from that of their White settler classmates. In examining the children's speech and classroom behaviour, it becomes apparent that silence, teasing, and story telling are important discourse characteristics of Indigenous English. The findings indicate that White settler educators demonstrate little awareness of the systematic linguistic and discourse characteristics of Indigenous English and that this lack of awareness is apparent in White settler educators' descriptions of their approaches to teaching, literacy development, classroom management, evaluation, and referral of First Nations and Metis students for speech and language assessment. Other findings include denial of difference, and a race/class divide in the school and community. Possible resolutions to the problems faced by these students may include teacher training and dialect awareness classes. This field has not been adequately explored and further research is needed to discover viable solutions to the issues experienced by dialect speakers of Indigenous English in the Standard English classroom.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".