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Record W1978145321 · doi:10.1080/09658416.2011.652633

Orality for all: an imaginative place-based approach to oral language development

2012· article· en· W1978145321 on OpenAlexaff
Mark Fettes

Bibliographic record

VenueLanguage Awareness · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOralityContext (archaeology)CurriculumPedagogySociologyNarrativeThe artsLiteracyLinguisticsVisual artsHistoryArt

Abstract

fetched live from OpenAlex

This paper reports on an innovative approach to oral language development in one British Columbia elementary school, in the context of a larger-scale research project aimed at building cultural inclusive classrooms through the development of imaginative teaching practices. A number of approximately three-week units were designed to lead students through a series of increasingly challenging oral language activities; each unit was developed on the basis of a traditional oral narrative of the Stó:lō, the aboriginal people of the region. In the tradition of design-based research, key features of the units are discussed in connection with pedagogical challenges encountered by the teachers using them. This approach to integrating oral language in the language arts curriculum was effective at promoting engagement by at least some marginalised students, but limited by cultural and political factors that were not addressed in the original research design. Conclusions are drawn for future research on imaginative oral language development.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.013
Scholarly communication0.0070.002
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.393
Teacher spread0.338 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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