Assessing organization performance in public sector systems: lessons from Canadas MAF and New Zealands PIF
Bibliographic record
Abstract
Performance frameworks established by central agencies for monitoring organisational performance have been instituted by surprisingly few governments. The empirical literature focuses largely on policy and program performance systems in government whereas comprehending organizational performance and capacity issues has received much less attention This chapter focuses on relatively rare and relatively recent efforts to introduce organization-performance systems, and explores the implications for practice, theory, and research. Two approaches are compared, namely Canada’s Management Accountability Framework and New Zealand’s Performance Improvement Framework. We ask, for example, what have we learned about organizational-level performance improvement and performance management systems initiated by central governments thus far? The chapter looks at how these two organizational-performance approaches fit into the large array of ‘performance systems’ (Bouckaert & Halligan, 2008) found in the Canadian and New Zealand governments. These two systems are surprisingly and intriguingly different from each other.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".