Measuring Success: Factors Impacting on the Implementation and Use of Performance Measurement within Victoria's Human Services Agencies
Bibliographic record
Abstract
Evaluation of performance is now an accepted human resource management practice in many organisations. The research reported in this paper identifies factors impacting on the implementation and use of performance measurement systems in Victoria's human services agencies. It applied a conceptual framework to evaluate the influence of rational/scientific and political/cultural factors on their implementation. The results indicate that both political/cultural and rational/scientific influences are present when human services organisations in Victoria implement and use performance measurement systems. While the results do not contain an exhaustive list of influences that may impact on an organisation's performance measurement efforts, they do provide confirmation of the existence of these two distinct categories of influences, and are useful in understanding how such factors may affect the operation of performance measures. This paper advances our knowledge of how to develop new approaches that more effectively manage the implementation and utilisation of performance measures. The results will be of interest to human service organisations, government departments that fund these agencies and to those with an interest in the accountability of publicly funded bodies.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".