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Record W2801015715 · doi:10.7939/r3sq8qw0m

“Don’t Step on Each Other’s Words”: Aboriginal Children in Legitimate Peripheral Participation With Multiliteracies

2017· article· en· W2801015715 on OpenAlexaboutno aff
Melanie Allison Brice

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceMedicinePsychologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

This study is an examination of the multiple literacy practices of four Aboriginal children in a Western Canadian prairie urban classroom. It is framed using sociocultural theory that posits that the literacy learning of children occurs in a social environment through a co-constructed, culturally relevant landscape. The purpose of this study was to explore how First Nations and Métis children whose teachers had identified them as successful readers, used multiliteracies to support their reading in an elementary language arts classroom. This research drew on the work of sociocultural learning theorists Lave and Wenger (1991) and their concepts of community of practice and legitimated peripheral participation, and Moll et al.’s (1992) concept of funds of knowledge. Statistics show an increasing literacy gap between Aboriginal students and other Canadian students, and there is an abundance of research on school failure and deficit language and literacy learning. However, Aboriginal children come to school with a great deal of knowledge and experience with different literacies, technology, and use language in ways that help them to successfully navigate school literacy. Therefore, the following research questions guided an exploration of the ways that the focal children used language, the knowledge and experiences that they brought with them into the classroom, and how they participated in literacy practices: (a) How did the funds of knowledge that the participants brought into the classroom support their literacy practices in the English language arts (ELA) classroom? (b) How the Aboriginal children’s oral language support their reading? and (c) How did the Aboriginal children in a classroom community participate as legitimate peripheral participants in reading while integrating multiliteracies? The researcher investigated these questions by using interpretive case study methodology and collecting data by observing and interviewing the participants and collecting student- and teacher-created artifacts. This research adds to the field of literacy learning and teaching because it demonstrates the importance of multiliteracies as a means of including diverse voices, texts, and cultures in school literacy. The use of multiliteracies creates a bridge between home and school literacy by giving minority children who might not have access to privileged forms of literacy a means of acquiring school literacy. Multiliteracies bring Aboriginal perspectives into literacy learning and validate the knowledge and experiences that Aboriginal children and youth bring to school. This research also addressed the application of Rosenblatt’s (1978/1994) reader response theory to all texts, including digital. The implications for teacher practice are the need for educators to move away from deficit theories of learning and stop viewing the literacies that Aboriginal children bring to school as problematic. Instead, educators need to provide spaces for Aboriginal children to talk about their lived experiences and acknowledge their knowledge as valid and valuable so that they can flourish.

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)0.003
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.747

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0240.016
Scholarly communication0.0060.003
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.252
Teacher spread0.242 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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