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Record W2591560250

Mobilizing Decolonized Nursing Education at Aurora College: Historical and Current Considerations

2016· article· en· W2591560250 on OpenAlexaffabout
Pertice Moffitt

Bibliographic record

VenueNorthern review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsAurora College
Fundersnot available
KeywordsIndigenousColonialismCurriculumSociologyNurse educationTraditional knowledgePedagogyPower (physics)Circumpolar starPolitical scienceNursingMedicineLawEcology
DOInot available

Abstract

fetched live from OpenAlex

Nursing education at Aurora College in the Northwest Territories, Canada has evolved from its beginnings as a diploma nursing program to today’s undergraduate degree program. The purpose of this report is to share the evolution of the program and the movement towards decolonized pedagogy and epistemology throughout its development. Since 51% of the territory’s population is Indigenous and the other 49% is diverse, traditional knowledge and different ways of knowing, along with cultural safety and competency, are important concepts for northern nursing. The concept-based curriculum lends itself to teaching and learning from a critical post-colonial perspective where students learn to critique and question colonial practices and dominant discourse. Focusing inquiry into colonial pedagogy of this nature will provide new insights and considerations of power and power relations within education. This report contributes to the topic of decolonizing nursing education at a time when there is little substantive effort in this direction. This report is part of a special collection from members of the University of the Arctic Thematic Network on Northern Nursing Education. The collection explores models of decentralized and distributed university-level nursing education across the Circumpolar North.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.490

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.007
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.072
GPT teacher head0.382
Teacher spread0.310 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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