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Record W1902032077 · doi:10.19173/irrodl.v15i1.1357

Post-secondary distance education in a contemporary colonial context: Experiences of students in a rural First Nation in Canada

2014· article· en· W1902032077 on OpenAlexafffundvenueabout
Jesse Simon, Kevin Burton, Emily Lockhart, Susan O’Donnell

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of New BrunswickAssembly of First Nations
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousContext (archaeology)Distance educationSituatedColonialismRural areaSociologyPedagogyPolitical scienceEconomic growthPublic relationsGeography

Abstract

fetched live from OpenAlex

<br /><p>Post-secondary distance education gives students and their families living in remote and rural regions the option to stay in their communities while they study instead of moving closer to the universities in cities. Post-secondary distance education is an option in many rural and remote First Nation (Indigenous) communities in Canada; however there are many challenges to successful adoption in these communities. There are also many opportunities for post-secondary institutions to expand their abilities and capacity in developing and delivering appropriate content supporting these unique, self-governing environments in Canada. We explore the experiences of students from a rural First Nation in Canada with post-secondary distance education, focusing on how different delivery methods offer both opportunities and challenges for community-based students. The study is situated in the context of contemporary colonialism in Canada.</p>

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 score0.060
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0370.012
Scholarly communication0.0090.002
Open science0.0030.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.079
GPT teacher head0.434
Teacher spread0.355 · 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

Citations23
Published2014
Admission routes4
Has abstractyes

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