Adoption Of The Balanced Scorecard By Municipal Governments: Evidence From Canada
Bibliographic record
Abstract
This paper examines the evolving adoption of the Balanced Scorecard (BSC) in municipal governments. We conduct a study of the use of BSCs in municipal governments across Canada. Senior administrators are surveyed regarding the use of performance measures and the results are compared to a similar study conducted in 2004. The results show that municipal governments continue to focus primarily on financial metrics. Adopters recognize the value of a BSC and most no longer see the BSC as a fad or as a set of ad-hoc measures. They recognize the BSC is a valuable tool that links the municipality¡¯s mission and strategy to objective measures. This paper extends the literature on the BSC by identifying a growing desire to improve performance measurement within Canadian municipalities. In addition, understanding the needs, concerns, and reasons for not implementing a BSC will provide practitioners with the necessary information to develop BSC tools that work for a municipal government
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".