Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.046 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".