Motives and incentives in the privatization of the South Australian Lotteries
Bibliographic record
Abstract
IMPACTThis case study illustrates the importance of political factors in determining policy outcomes. In addition to technical and policy considerations, practitioners must consider the political aspects of policy changes and incorporate them into their assessments. Core political considerations were incorporated into the successful privatization of the South Australian Lotteries, which were derived from an earlier unsuccessful attempt. Public servants must develop sharp political skills in order to align with the strategies of political decision-makers.ABSTRACT This paper investigates the motives and incentives behind the privatization of the South Australian Lotteries. Our analysis yields three main insights: first, short-term financial considerations were the most important incentive in this instance of privatization. Second, a mix of instrumental and political considerations co-existed as motivating factors. Lastly, a previous (unsuccessful) privatization effort provided important guidance. These insights reveal the inadequacy of the established literature, which focuses on single-motive explanations of privatization, to account for special cases.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".