Accountability Disclosures by Queensland Local Government Councils: 1997–1999
Bibliographic record
Abstract
The annual report is promoted and regarded as the primary medium of accountability for government agencies. In Australia, anecdotal evidence suggests the quality of annual reports is variable. However, there is scant empirical evidence on the quality of reports. The aim of this research is to gauge the quality of annual reporting by local governments in Queensland, and to investigate the factors that may contribute to that level of quality. The results of the study indicate that although the quality of reporting by local governments has improved over time, councils generally do not report information on aspects of corporate governance, remuneration of executive staff, personnel, occupational health and safety, equal opportunity policies, and performance information. In addition, the results indicate there is a correlation between the size of the local government and the quality of reporting but the quality of disclosures is not correlated with the timeliness of reports. The study will be of interest to the accounting profession, public sector regulators who are responsible for the integrity of the accountability mechanisms and public sector accounting practitioners. It will form the basis for future longitudinal research, which will map changes in the quality of local government annual reporting.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".