Alert, but not alarmed: Electoral disinformation and trust during the 2023 Australian voice to parliament referendum
Bibliographic record
Abstract
Abstract In 2024 experts highlight misinformation and disinformation “amid elections” as the top short‐term global risk. In addressing this pressing concern, electoral authorities are devising strategies to counter electoral disinformation while governments consider changes to public policy and legislation. Drawing on motivated reasoning theory, this study assesses the impact of disinformation and mitigation measures in Australia during the 2023 referendum campaign – to establish a constitutionally enshrined Indigenous Voice to Parliament – and its subsequent impacts on trust in the Australian Electoral Commission (AEC). Through a nationally representative survey experiment (N = 3825) we find overall high public trust in the AEC with disinformation having a small, but detectable effect. This study finds a level of “moral panic” regarding disinformation's threat to electoral integrity, at least in the Australian setting. However, concerningly, we also find existing AEC communication and refutation strategies have limited impact on countering distrust arising after a disinformation attack, suggesting a need for other strategies. Nonetheless, the study underscores the resilience of Australian electoral processes against disinformation threats serving as a caution against excessive legislative reaction to this global problem. Our study contributes to understanding the complex interplay between information, trust, and public policy responses to disinformation challenges.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".