Bibliographic record
Abstract
Purpose This paper examines the actual and desired use of performance measures for management and external reporting purposes, as well as perceived impediments to their effective use. Design/methodology/approach A total of 334 senior administrators in Canadian municipalities participated in this survey study. Findings Somewhat more efficiency measures than effectiveness measures have been used for various purposes. However, greater use was perceived desirable than actually occurred, particularly for effectiveness measures. A significant increase in the use was expected in the near future especially for effectiveness measures. Internal and external verification of measures was considered important by both internal and external auditors. Although the study also identified impediments to the development and meaningful use of performance measures, performance measurement appears to have been accepted as a useful managerial tool and have significant future potential. Research limitations/implications The results are limited by the survey method. Practical implications The results can provide guidance to public‐sector administrators and professionals for planning and decision making purposes and to professional bodies and regulatory agencies for developing comparative performance reporting standards. Originality/value Using the descriptive and normative perspectives, this study provides new evidence in the Canadian context. It concludes that, although the mandatory performance measurement and reporting requirements for municipalities in Canada lag those in the UK, the USA, and Australia, a significant degree of usage occurred voluntary in Canadian municipalities.
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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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".