Focusing on Inputs, Outputs, and Outcomes: Are International Approaches to Performance Management Really so Different?
Bibliographic record
Abstract
Abstract: The focus of performance information appears, on the surface, to differ among Canadian, Australian, and US federal governments. While these countries emphasize different aspects of performance, their federal guidelines on performance measurement share important common ground — the logic model. A performance logic model helps clarify the linkages between inputs, activities, and process and outputs, short- and long-term outcomes, and impacts. The model assists both analysts and managers to articulate the cause-effect theory of a program or service and should answer the fundamental questions about WHY an initiative exists, WHAT the expected outcomes are, WHO the program or service will reach and HOW it will be delivered. There is a tendency for organizations to focus on measurement before first describing the logic of their enterprise. International practice suggests that Canadian, Australian, and US approaches all promote the understanding of program logic before measurement. Such an understanding will be key to the successful implementation of performance management initiatives in each of these countries.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".