Unravelling the Mysteries of the Oracle: Using the Delphi Methodology to Inform the Personal Tax Reform Debate in Australia
Bibliographic record
Abstract
The paper explores key outcomes relating to personal income tax (PIT) reform in Australia derived from the use of a Delphi methodology conducted during 2006. The Delphi methodology combines quantitative and qualitative techniques to explore future possibilities in systematic and iterative rounds of anonymous testing involving a panel of international experts in the field of personal taxation. The experts have been drawn from Australia and from countries with comparable PIT regimes, such as the UK, the USA, Canada and New Zealand. Over a four month period the panel members responded to a series of open-ended propositions relating to the design and operation of the PIT, with a view to establishing whether a consensus on key PIT reform issues could be developed. Studies comparing the Delphi's results with other methods have confirmed the effectiveness of the methodology on the basis of both its capacity to generate ideas and its effective use of participants' time. This paper considers the methodology used and also focuses on the outcomes of the process, showing how these outcomes are being used to inform the final phase of a broader research project into personal tax reform in Australia which is being conducted with funding from the Australian Research Council and support from CPA Australia.
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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 | 0.043 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".