AASB Research Centre Working Paper No. 26-03: Service Performance Reporting - Insights from Domestic and International Experience
Bibliographic record
Abstract
This report provides input into any decisions to be made about the development of a future service performance reporting pronouncement in Australia by assessing the potential viability, design, implementation and challenges of a national framework for the private not-for-profit (NFP) sector. It draws on a multi-method approach, including: • An analysis of 309 annual reports from NFPs across Australia, New Zealand, the UK, Canada, the US, and South Africa • Focus groups involving donors, preparers, auditors, regulators, directors, and representatives from peak bodies • Sector-wide survey data on reporting needs and challenges The study addresses four objectives: identifying current best practices of service performance reporting by NFPs, evaluating the feasibility and challenges of assurance, drawing lessons from international jurisdictions, and assessing the suitability of a reporting framework for the diverse Australian NFP landscape. The report also provides guidance toward a reporting model that is proportionate, credible, and fit-for-purpose.
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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 | 0.032 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".