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Record W3120826085 · doi:10.56421/ujslcbr.v10i0.315

The Role of Relationships in Indigenous Community-engaged Learning

2020· article· en· W3120826085 on OpenAlexafffundabout
Érica de Souza e Souza, Shantel Watson

Bibliographic record

VenueUndergraduate Journal of Service Learning & Community-Based Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Toronto
FundersJackman Humanities Institute, University of Toronto
KeywordsIndigenousGrassrootsService-learningPhotovoiceIndigenous educationSociologyCurriculumParticipatory action researchPedagogyResidencePolitical sciencePublic relationsEconomic growthPoliticsEcology

Abstract

fetched live from OpenAlex

Service Learning is the pursuit of education while serving the interests of the community, and undertaking a direct role in social change (Phillips, 2013). It allows students a chance to develop research skills while simultaneously building bonds with colleagues and creating relationships within communities. We are two undergraduate students, from a collaborative group of five, who undertook a month-long summer Scholars in Residence (SiR) program at the University of Toronto Mississauga. Our involvement with the SiR project, “Community-engaged Learning with the Indigenous Action Group (IAG)” immersed us in a grassroots effort to research the impacts of a community-engaged learning course at the student level. The goal of the course, “Anthropology and Indigenous Peoples of Turtle Island” (ANT241H) is to initiate and maintain an Indigenous curriculum in a university setting. Self-reflection is pivotal in our research skill development, and our understanding of the impact of the project in its initial stages. Qualitative analysis of student assignments demonstrated the importance of creating relationships with Indigenous Scholars to advance Indigenous Pedagogy and provide students with the tools needed to build relationships with the local Indigenous community. Our own relationships with the Mississaugas of the Credit First Nation (MCFN) was limited to remote learning by COVID-19 pandemic restrictions. Despite the hindrances to our relationship building, the SiR experience transcended our interpersonal needs as allies, and laid the foundation for a stronger bond as we progress in the longitudinal study.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.127
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1270.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0370.001
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0000.074
Insufficient payload (model declined to judge)0.0000.000

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 head0.240
GPT teacher head0.383
Teacher spread0.142 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes3
Has abstractyes

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