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Record W2338917634 · doi:10.15402/esj.v4i1.313

Community-University Engagement: Case Study of a Partnership on Coast Salish Territory in British Columbia

2018· article· en· W2338917634 on OpenAlexvenueaboutno aff
Margaret Bain

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipIndigenousContext (archaeology)Community engagementSociologyRelevance (law)Public relationsPolitical scienceGeographyEcology

Abstract

fetched live from OpenAlex

In the context of expanding community engagement efforts by universities and growing awareness of the past and current impacts of settler-colonialism in Canada, this study explores one Indigenous-settler, community-university partnership. Building on a framework of community-university engagement and decolonization, this case study explores a partnership between Fraser Valley Aboriginal Children and Family Services Society (Xyolhemeylh) and the Division of Health Care Communication at the University of British Columbia (UBC-DHCC). This partnership, called the “Community as Teacher” program, began in 2006 and engages groups of UBC health professional students in three-day cultural summer camps. This qualitative case study draws on analysis of program documents and interviews with Xyolhemeylh and UBC-DHCC participants. The findings of the study are framed within “Four Rs”—relevance, risk-taking, respect, and relationship-building—which extend existing frameworks of Indigenous community-university engagement (Butin, 2010; Kirkness & Barnhardt, 1991). Committed to a foundation of mutual relevance to their missions, both community and university partners undertook risk-taking, based on their respective contexts, in establishing and investing in the relationship. Respect, expressed as working “in a good way,” likewise formed the basis for interpersonal relationship-building. By outlining the findings in relation to these four themes, this study provides a potential framework for practitioners and researchers in Indigenous-university partnerships

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)0.005
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 score0.224
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0440.007
Scholarly communication0.0050.002
Open science0.0030.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.199
GPT teacher head0.403
Teacher spread0.203 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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