Reform and Resistance: The Regulation of Government Advertising in
Bibliographic record
Abstract
In Australia, a laissez faire approach to regulating government advertising allowed the Howard Government to spend over AUS$1 billion on advertising between 1996 and 2005 despite ongoing accusations of misuse for partisan benefit; two Auditor-General reports, three Parliamentary inquiries and three Private Members ’ Bills calling for stronger regulation; and a failed High Court challenge. This paper explains the current system of Australian regulation at the federal level, shows how this is out of step with international practice (particularly the UK, US, Canada and New Zealand) and traces the history of (failed) calls for comparable reforms. It considers why consistent efforts to reform the regulatory system from so many external actors via different methods and over such a long time period, have been consistently unsuccessful with recommendations ignored or rejected by the federal government. It concludes that this is a case of policy making ‘in a cold climate ’ where the ruling party benefits from existing rules and is extremely reluctant to change those rules. Exploring this political context in more depth, as well as examining what propelled reform in other jurisdictions, then allows us to consider what (if anything)
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".