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Record W2907465319 · doi:10.25071/1916-4467.40348

In a Good Way: Reflecting on Humour in Indigenous Education

2018· article· en· W2907465319 on OpenAlexaffvenue
Shannon Leddy

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIndigenousFace (sociological concept)SociologyAestheticsIntervention (counseling)PedagogyDecolonizationPsychologySocial sciencePolitical scienceArtPoliticsLaw

Abstract

fetched live from OpenAlex

Humour is ubiquitous in Indigenous communities, and often provides some of the most memorable moments in our relationships with one another. In this article, I explore an instance of such humour backfiring in an educational situation, and reflect on whether humour was an appropriate response. After surveying some academic research in the area of humour in the classroom, as well as some of the works of several prominent Indigenous writers and comedians, I reflect on the importance of humour in Indigenous pedagogy. Drawing on this research, and moments from my own practice, I theorize that humour has three core pedagogical impacts. First, it has a humanizing affect, helping us to see one another more clearly, and to appreciate that we all have foibles, and areas of ourselves that require improvement. It is also a culturally relevant pedagogy, having been used for millennia as a mechanism of social order and of upholding community values in Indigenous communities. Finally, humour also has a soothing effect, especially in the face of grappling with difficult concepts and situations, and can ease the tensions that often arise in Indigenous education classrooms. Used judiciously, humour is a powerful tool for decolonization. While I do not presume to offer a prescription for the use of humour in the classroom, in reflecting on my own practice, I am increasingly convinced of its importance in Indigenous pedagogy, and I offer my reflections for the reader’s consideration.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.414
Teacher spread0.362 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2018
Admission routes2
Has abstractyes

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Same venueJournal of the Canadian Association for Curriculum StudiesSame topicEducation and Critical Thinking DevelopmentFrench-language works237,207