Let Your Plan do the Driving: Effective Performance Measurement
Bibliographic record
Abstract
How do we know we are achieving the goals of a long term vision such as a Transportation Master Plan? The City of Edmonton in Alberta, Canada has developed a governance framework that links the Transportation Master Plan with an Implementation Plan and Performance Measures with Targets. These plans are driving the direction of Transportation Services Department and this is a significant change in the organization. Performance measures and targets were developed to align with the goals of the plan. Keeping a planning document alive and having the plan drive your organization’s decision making can be a challenge. However, creating a process that links the Vision with the budget process through performance measures allows for governments to select projects that will maximize the benefits for citizens and direct the city in the direction of the long term vision. This process is more important in recent times because tight budgets require governments to make difficult spending trade offs. The governance framework developed by the City of Edmonton and implemented for the 2012 to 2014 Capital Budget is one example of a successful performance measurement framework that has had a positive influence on the organization’s decision making process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".