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Record W3125811578 · doi:10.37333/001c.18585

Relational Learning With Indigenous Communities: Elders’ and Students’ Perspectives on Reconciling Indigenous Service-Learning

2020· article· en· W3125811578 on OpenAlexaffabout
Andrea Kennedy, Katharine McGowan, Gabrielle Lindstrom, C. L. Cook, Yasmin Dean, James Stauch, Cheryl Barnabé, Stephen Price

Bibliographic record

VenueInternational Journal of Research on Service-Learning and Community Engagement · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsIndigenousService-learningReciprocity (cultural anthropology)SociologyPsychologyPedagogySocial psychology

Abstract

fetched live from OpenAlex

This study counterbalances Western-derived evidence by describing Elders’ and students’ perspectives of Indigenous service-learning through Indigenous research methodology. Data collection took place in a midsize Canadian university after an Indigenous service-learning public networking forum. The purposive sample consisted of three Indigenous elders and five Indigenous students. Immediately following the event, Elders participated in a focus group, and then students completed a survey. Qualitative themes were interpreted using conversational method and relational analysis. Elders called for the replacement of the term service-learning, re-rooting of the term Indigenous, and respect for the Elders’ roles and knowledges. Interconnected themes by Elders and students signaled a necessary shift from service-learning to relational learning. Such connections reveal the core purpose of relational learning with Indigenous communities as maintaining good relations through humility, respect, honesty, and reciprocity while responding to the interconnected priorities of the land, traditional ways, Elders, and common language. Findings signal decolonizing opportunities for relational learning with Indigenous communities. Este estudio contrarresta la evidencia derivada de occidente al describir las perspectivas de Maestros ancianos (llamados “Elders” entre los pueblos indígenas norteamericanos) y aprendices sobre el aprendizaje-servicio indígena a través de la metodología de investigación indígena. La recolección de datos se llevó a cabo en una universidad canadiense de tamaño mediano tras la celebración de un foro de networking público de aprendizaje-servicio para indígenas. La muestra útil consistió en tres Maestros y cinco aprendices indígenas. Inmediatamente después del evento, los Maestros participaron en un focus group de una hora y los estudiantes completaron una encuesta. Los temas cualitativos se interpretaron utilizando el método conversacional y el análisis relacional. Los Maestros pidieron la sustitución del término aprendizaje-servicio, la recuperación del término indígena y el respeto hacia los conocimientos y el rol de los Maestros. Las conexiones entre los temas discutidos por Maestros y estudiantes demostraron la necesidad de un cambio del aprendizaje-servicio al aprendizaje relacional. Dichas conexiones revelan el propósito central del aprendizaje relacional con las comunidades indígenas, como mantener buenas relaciones a través de la humildad, el respeto, la honestidad y la reciprocidad mientras se responde a las prioridades interconectadas de la tierra, las formas tradicionales, los Maestros y el lenguaje común. Los hallazgos señalan oportunidades de descolonización para el aprendizaje relacional con comunidades indígenas.

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)0.010
metaresearch head score (Gemma)0.008
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0140.015
Scholarly communication0.0070.005
Open science0.0020.013
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.225
GPT teacher head0.419
Teacher spread0.194 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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