Storytelling in the Australian 2023 voice referendum campaign
Bibliographic record
Abstract
Personal stories are a strategic tool often used by advocacy movements to pursue claims for equality. In the 2023 Voice referendum campaign in Australia, personal storytelling was used by the conservative No campaign to argue against the constitutional recognition of Aboriginal and Torres Strait Islander peoples. Through narrative analysis of the Yes and No campaigns, we highlight two storytelling dynamics. First, the autobiographical hero narrative, fused with the Australian ‘fair go’, to de-historicise inequality and de-emphasise experiences of colonisation and systemic racism. Second, personal storytelling’s strength in emphasising shared identity between storytellers and the public helped the No campaign’s defence of the status quo and their claims that constitutional recognition would be divisive. These narratives set the agenda for the campaign, making it difficult for the Yes campaign’s use of community strengths-based stories to convince the public that recognition of difference was key to achieving greater equality.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".