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Record W2808504316 · doi:10.15402/esj.v4i1.314

Indigenous Methods and Pedagogy: Revisiting Ethics in Community Service-Learning

2018· article· en· W2808504316 on OpenAlexvenueno aff
Swapna Padmanabha

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
KeywordsService-learningTransformative learningIndigenousSociologyFraming (construction)PedagogyReciprocity (cultural anthropology)Engineering ethicsSocial scienceEngineering

Abstract

fetched live from OpenAlex

This paper looks at the development of a teaching module intended to enhance students’ understanding of ethics in a community service-learning (CSL) class. This module, created to meet academic (western) learning outcomes for CSL, is based upon Indigenous pedagogy and methods, and offers a non-western framing of specific community service goals, particularly reciprocity and transformative dissonance. The paper proposes that moving toward Indigenous or other ways of knowing offers students and instructors an entry point into decolonizing practices and into alternate ways of experiencing service, transformative learning, and power dynamics. The paper also includes a discussion of the theory behind the teaching module and focuses on the intertwining of ethical research protocols (from Tri-Council policy, OCAP® principles, and elsewhere), service-learning goals, and Indigenous methods within the context of settler colonial practices and policies. Alongside other traditional service-learning outcomes, the primary goal of the module is to encourage students to become critical thinkers reflecting on the mechanics of power and social inequity as they experience social justice founded upon the ideals of relationship building.

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)0.952
metaresearch head score (Gemma)0.761
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9520.761
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.7480.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.863
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 head0.275
GPT teacher head0.528
Teacher spread0.253 · 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
DomainMethods
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 routes1
Has abstractyes

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