Laughter with purpose: how First Nations Australian comedians use humour to engage, educate, and empower audiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it