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Record W3211586376 · doi:10.1080/08873631.2021.1999007

Exploring Indigenization and decolonization in cross-cultural education through collaborative land-based boundary education

2021· article· en· W3211586376 on OpenAlexaffabout
Melanie Zurba, George Land, Ryan Bullock, Bridget Graham

Bibliographic record

VenueJournal of Cultural Geography · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsAssembly of First NationsUniversity of WinnipegDalhousie University
Fundersnot available
KeywordsBoundary objectIndigenizationBoundary (topology)IndigenousBoundary-workSociologyWork (physics)Context (archaeology)DecolonizationPoliticsIndigenous educationPedagogyEngineering ethicsPolitical scienceSocial scienceEngineeringGeographyAnthropology

Abstract

fetched live from OpenAlex

This article considers the potential for collaboratively produced boundary education as an advancement of the boundary work concept in academia. The boundary work process aims to support collaboration that works around social, cultural, political, epistemological, and other forms of boundaries. We explore how education can act as a boundary object through the development and implementation of a pilot project for land-based educational programming offered through a university. In particular, we share observations arising through a land-based education initiative that engaged Indigenous land stewards from Wabaseemoong Independent Nation and students from The University of Winnipeg. Our approach is grounded in an extended conceptual framework and process for conducting boundary work in the context of collaborative educational design and implementation involving educators from academia and Indigenous community partners. The pilot project provided baseline insights for future boundary education collaborations and provided some direction for future work.

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)0.016
metaresearch head score (Gemma)0.016
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 score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0120.025
Scholarly communication0.0100.009
Open science0.0030.026
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.369
Teacher spread0.314 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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