Incapacitating, or something else? Unpacking Australian amendment culture in the First Nations Voice Referendum
Bibliographic record
Abstract
Abstract On October 14, 2023, Australians participated in a once-in-a-generation constitutional referendum to consider establishing a First Nations advisory body in the Constitution. The proposal was rejected, meaning that only eight of the forty-five amendments put to the Australian people have been approved since 1901. This article tests the viability of amendment culture as an explanation for Australians’ reluctance to change the Constitution. Using original large-n survey data from the Australian Constitutional Values Surveys (2008–21), the article examines Australians’ openness to constitutional change over time, highlighting that despite a long history of rejecting constitutional amendments, Australians are not inherently opposed to constitutional change. Instead, a tendency to emphasize the Constitution’s practical role makes Australians susceptible to technical arguments designed to stifle support for change. In addition to providing insight into the Voice referendum result, the article makes three contributions to the amendment culture literature. First, it presents a model for case-study analysis of constitutional change, offering a detailed examination of amendment culture. Second, it provides a long-called-for large-n survey of attitudes toward constitutional change. Third, it demonstrates the importance of contextualizing amendment culture within broader studies of political culture.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".