Measurable Perfomance Indicators for Roads: Canadian and International Practice
Bibliographic record
Abstract
Performance indicators are an essential part of modern road asset management. The basic rationale for having measureable performance indicators is that limited availability of resources makes it necessary to allocate these resources as effectively as possible among competing alternatives; moreover, that considerations of safety, capacity, serviceability, functionality and durability are explicitly recognized. A comprehensive approach to developing performance indicators should consider the basic rationale, a balance in use and reporting, efficiency and effectiveness, a tie to transportation values, objectivity in the measurements used and the stakeholders involved in the development of a framework. A basic framework for the roads sector which represents a consolidation of international and Canadian practice is presented and consists of: general macro-level overview, and detailed level involving: (a) service quality provided to road users, and (b) institutional productivity and effectiveness. Other examples of performance indicators from OECD, Australia and the United States are also presented. Performance indicators should be tied to an agency's policy objectives and to implementation targets or minimum acceptable levels of performance, as described in detail in the paper. Finally, a comprehensive set of performance indicators for roads, matched to assessment criteria, as part of a new initiative on Development of a Framework for Assessment of the State, Performance and Management of Canada's Core Public Infrastructure are presented.
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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 | 0.045 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.033 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".