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Record W7034498738

Valuing linguistic diversity: grammatical features of First Nations school-aged children's spoken and written language

2019· dissertation· en· W7034498738 on OpenAlexfundaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsnot available
FundersUniversity of Victoria
KeywordsIndigenousAffect (linguistics)Variety (cybernetics)NarrativeLiteracyGrammarWritten languageIndigenous languageFirst language
DOInot available

Abstract

fetched live from OpenAlex

Students who speak local varieties (i.e., dialects) of English that differ from the codified variety promoted in school are at a disadvantage. Research illustrates that differences in sound systems, grammar, vocabulary, and usage can negatively affect literacy development and achievement in math and science, and lead to misunderstandings and changes in teacher attitudes toward students. Moreover, the use of inappropriate assessment tools may result in unnecessary pathologization and inappropriate pedagogical approaches. Since many Indigenous children may speak local varieties, it is reasonable to assume that the same issues that hinder school success for speakers of other varieties affect many Indigenous students in Canada in similar ways. However, to date, research concerning Indigenous Englishes in Canada is scant. Similarly, virtually no empirical evidence has been gathered on use in Canadian schools. By extension, the trajectory of use of features as children progress through grades remains unknown. The goal of this research was to begin to address the crucial necessity of learning more about Indigenous English varieties, in order that appropriate language assessment and pedagogical practices can be implemented. The research, conducted in a remote community in Northern British Columbia, Canada, concentrates on differences in grammar used by a group of First Nations school-aged children. I analyzed oral narrative language samples of Kindergarteners, and oral and written narrative language samples of students in Kindergarten to Grade 5, over a three-year period. Results reveal the presence of at least 23 distinct grammatical features, many of which may have been influenced by the structure of the ancestral language. At school entry, students used grammatical features at high rates, regardless of whether or not they later required speech-language pathology or special education services. As children progressed through the grades, the rate at which they produced features appeared to follow a curvilinear trajectory, declining until grades 3 and 4 and then gradually rising again in middle school. A preference for using shorter sentences with less use of subordination and embedding of clauses also appears to be a feature of this variety. Most of the features the children used in their speech, they also used in their writing. Children had the most difficulty switching to standard English forms of verb tense, and so verb tense may require more direct instruction. While my results may not be directly generalizable to other First Nations communities, it is anticipated that educators will use them as a guide in their practice and instruction, so they can cease confusing features of a local variety with errors requiring “correction”, avoid unnecessary pathologization, and adjust expectations regarding the rate at which children can be expected to acquire the codified standard language model. It is also hoped that this study will contribute to the preservation and celebration of the unique ways of speaking English that have evolved in northern communities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.293
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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