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Evidence of Co-learning through a Relational Pedagogy: Indigenizing the Curriculum through MIKM 2701

2019· article· en· W2948832264 on OpenAlexaffvenue
Emily Root, Stephen Augustine, Kathy Snow, Mary Beth Doucette

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsCape Breton University
Fundersnot available
KeywordsCurriculumPedagogyCourse (navigation)PsychologyMedical educationMathematics educationMedicineEngineering

Abstract

fetched live from OpenAlex

In the winter term of 2016, Cape Breton University launched a revised version of a second year Mi’kmaw Studies course entitled Learning from the Knowledge Keepers of Mi’kmaki (MIKM 2701). This course was designed to be led by local Elders and Knowledge Keepers with facilitation support from university faculty. It was designed by course facilitators as a dual-mode course, with the opportunity for students to participate face-to-face and online, and the excitement it generated quickly went “viral.” In this paper, we describe the experiences of the participants in the course through an analysis of their own reflections on the 13 weeks of instruction. The aim of this analysis is to share course design considerations for post-secondary institutions attempting to “Indigenize the academy” at a course level, but also to evaluate the process of co-learning as it was evidenced in the course as a means to address educational complexity and decolonization efforts in the classroom.

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)0.009
metaresearch head score (Gemma)0.018
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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.013
Scholarly communication0.0090.004
Open science0.0020.012
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.138
GPT teacher head0.421
Teacher spread0.283 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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