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Record W2592705848 · doi:10.4018/ijmbl.2017040103

Mobile Learning and Indigenous Education in Canada

2017· article· en· W2592705848 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Mobile and Blended Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsIndigenousCurriculumIndigenous educationContext (archaeology)PedagogyInformal learningModalitiesEducational technologyMobile technologyBlended learningSociologyMobile devicePsychologyComputer scienceGeographySocial scienceWorld Wide Web

Abstract

fetched live from OpenAlex

M-Learning holds great potential for supporting the positive educational outcomes of underserved Indigenous communities in the Candian North, and even in urban centers, that are at risk of exclusion from affordable, high-quality learning experiences. The technical advantages of having mobile technology to deliver educational curricula and assess outcomes, however, must not overshadow the continuing need for culturally relevant teaching modalities that work for Indigenous learners. When used innovatively, mobile learning can be integrated successfully into a context of existing practices, beliefs, experiences, and values related to Indigenous epistemologies and pedagogies. These mobile technologies are not only helping Indigenous learners to develop new media aptitudes, they are providing an opportunity for learners and instructors to develop stronger links between formal and informal learning opportunities, building on the inherently mobile and contextual traditions of Indigenous peoples.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.276
Teacher spread0.270 · 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