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Record W2798184381 · doi:10.7939/r38324

A portrait of Aboriginal elementary school classrooms: an exploratory study using elements of ethnographic research design

2010· article· en· W2798184381 on OpenAlexaboutno aff
Haneef Abdulrehman

Bibliographic record

VenueUniversity of Alberta Library · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyPortraitExploratory researchMathematics educationSociologyPedagogyPsychologyVisual artsArtAnthropology

Abstract

fetched live from OpenAlex

The objective of this exploratory, qualitative study was to obtain a greater understanding of educational issues experienced by teachers and students in the context of two rural Aboriginal elementary schools. Using elements of ethnographic methodology including participant-observer interactions and interviews, the data were collected from two geographically and contextually disparate elementary schools in Alberta serving predominantly Cree student populations. Surface analysis of the data revealed that challenges for teachers and students fell into either environmental or academic classification and included chronic absenteeism, transiency and problems pertaining to language mastery and reading readiness. The principal benefit identified for teachers was high job satisfaction and, for students, a safe environment where basic needs are met and programming is reflective of traditional Aboriginal worldviews. Deep Analysis delved into the role of culture in the development of the student and community; implications, practical applications, and further directions for research were discussed.

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)0.003
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 score0.826
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0170.008
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0010.002
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 head0.059
GPT teacher head0.340
Teacher spread0.281 · 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
Published2010
Admission routes1
Has abstractyes

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