Benchmarking the financial performance of local councils in Ireland
Bibliographic record
Abstract
Abstract It was over a quarter of a century ago that information from the financial statements was used to benchmark the efficiency and effectiveness of local government in the US. With the global adoption of New Public Management ideas, benchmarking practice spread to the public sector and has been employed to drive reforms aimed at improving performance and, ultimately, service delivery and local outcomes. The manner in which local authorities in OECD countries compare and benchmark their performance varies widely. The methodology developed in this paper to rate the relative financial performance of Irish city and county councils is adapted from an earlier assessment tool used to measure the financial condition of small cities in the US. Using our financial performance framework and the financial data in the audited annual financial statements of Irish local councils, we calculate composite scores for each of the thirty-four local authorities for the years 2007–13. This paper contributes composite scores that measure the relative financial performance of local councils in Ireland, as well as a full set of yearly results for a seven-year period in which local governments witnessed significant changes in their financial health. The benchmarking exercise is useful in highlighting those councils that, in relative financial performance terms, are the best/worst performers.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".