Defining Quality Planning Outcome Criteria for the City of Calgary
Bibliographic record
Abstract
How do we define the quality of urban development? What outcomes should good development have? How can our planning processes secure those outcomes? These are the questions that areraised in this research project. Definitions of quality development outcomes can vary between different individuals and situations; however, for a municipality such as the City of Calgary thegoal is to provide a clear direction on what outcomes of development are desirable. To do this requires the adoption of plans and policies that reflect municipal priorities and are at the sametime aligned with standard criteria for quality in urban development. Once criteria are put in place, municipalities can monitor development and evaluate the extent to which developmentoutcomes match these criteria of quality. Using the case of the City of Calgary, this research project provides recommendations on how criteria for the quality of development outcomes canbe set and then applied in the existing planning and regulation process. These recommendations were generated by means of a review of the literature, an analysis of plans, policies, andprocesses, and interviews with City of Calgary planning staff. They provide the City of Calgary with guidelines to consider when designing quality criteria for development outcomes andprocesses for evaluation and monitoring.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".