A Framework for Measuring Accessibility as a Metric of Quality of Life in Polycentric Cities
Bibliographic record
Abstract
The concept of quality of life has been an ongoing subject of discussion—both theoretical and empirical—in the field of urban development. There is strong subjective (opinion-based) evidence suggesting the existence of a link between an individual’s perception of their living environment and their quality of life. However, setting up an experimental framework for measuring quality of life is challenging since this type of investigation requires researchers to first answer the question of what factors could impact an individual’s perception of quality of life, in particular those related to neighbourhood development and available services. It is important to note that, if appropriately chosen, factors affecting quality of life as it pertains to land development and land use can serve as metrics for urban developers and municipal planners in building attractive neighbourhoods. This, in turn, will lead to thriving cities/municipalities, and will promote sustainable social and economic development. This thesis presents a methodology to measure the effect of neighbourhood development on the quality of urban life of residents, and assesses the impact of combining objective (quantitative) and subjective (qualitative) variables to evaluate quality of life in select neighbourhoods of a polycentric city (i.e., a city with more than one hub, or sub-centre, of services and activity). A case study that involves four neighbourhoods in Edmonton, Alberta, Canada, is used to demonstrate the effectiveness of the proposed methodology and illustrate its essential features.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".