An integrated approach to capital budgeting: the City of Prince George
Bibliographic record
Abstract
The approval of myPG part 1 by the City of Prince George Council is a catalyst for change with respect to a number of City actions, including capital budgeting. The availability of social, environmental and economic goals specific to the community provides an opportunity to align capital decision framework with a revised information structure which separates community from municipal service goals. The separation of community and municipal service goals creates the need for goals specific to the City at an organizational level. I have collaborated with the myPG implementation team to develop four preliminary municipal service goals that leverage existing objective data specific to the City. The revised information structure also provides the opportunity to integrate the core focus areas identified in City Council's three year strategic plan into the capital budget decision framework. Review of the existing capital budget decision framework identified other opportunities to improve the structure and scoring systems. Consolidating the score recording document with the goal document improves the framework structure by increasing the connection between each goal and the score assigned by the user. This reduces the likelihood that the user will assign an arbitrary value to a goal category. A Likert scale was used to improve the previous pass/fail scoring system by allowing the user to communicate the magnitude a project will contribute towards each goal. Unlike community goals and Council's core focus areas municipal service goals did not exist and were developed for the purpose of this paper. Accordingly, I tested the impact of these goals by evaluating the types of projects selected by each goal category when I ranked the 2011 capital project list for each goal category independently. The results show that the newly created municipal service goals selected less new asset projects than community and strategic goals. Since new asset projects create long term maintenance and renewal liabilities, the addit
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".