Laughter with purpose: how First Nations Australian comedians use humour to engage, educate, and empower audiences
Bibliographic record
Abstract
This essay employs a qualitative, culturally grounded methodology centred on interviewing Aboriginal and Torres Strait Islander comedians, writers, and performers to understand how Blak humour is used to engage, educate, and empower audiences in Australia. There is very little published research on First Nations Australian humour, despite its significance. I employ ‘Blak’ comedy and humour as an educational tool to facilitate truth-telling and to promote and evoke deeper engagement with, and understanding of First Nations Australian history and culture. Inspired by Destiny Deacon, I embrace ‘Blak’ as a term of self-determination, reflecting authentic First Nations identity. Building on this, I define ‘Blak’ as a distinct comedic genre, emphasising its role in expressing Aboriginal perspectives and resistance. This aligns with my framing of ‘Blak’ as a unique comedic genre, distinct from ‘black comedy’, which traditionally explores morbid themes. Aboriginal humour embraces both ‘Blak’ and ‘Black’ elements, showcasing its depth and cultural specificity. There are also terms used throughout the essay like ‘mob’, ‘our mob’, ‘Blak fullas’ or ‘fullas’, that refer to Aboriginal and Torres Strait Islander people. These are the words and phrases we commonly use to describe and identify ourselves within our communities.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".