Planning for a quality life: A theoretical approach to quality of life research in community planning
Bibliographic record
Abstract
For many years measuring Gross Domestic Product (GDP) has been the standard to which success has been defined by cities and countries alike. Economic brilliance however, can only take us so far. Measuring quality of life (QOL) is quickly becoming the preferred way of measuring success in the built environment. But what does it mean? QOL is synonymous with terms like ‘happiness’, ‘well-being’ or ‘life satisfaction’, but includes a more holistic evaluation of them. The purpose of this research is to shed light on why it is we do the things we do, and to consider the role of a planner in a pragmatic way. All too often planning is considered to be idealistic where decisions are made on the basis of “best practices”. What this research intends to answer is; who are these ‘best practices’ looking to serve? Have we succeeded in doing what we set out to do? And to understand that just because a decision was made in the past to serve a particular goal, it may not equate to how we should be considering that goal now. What we should be asking ourselves is; what does success look like? Can we define it? Can we measure it? Are we happy? The objective of this research is not only to understand if measuring QOL can better inform policy decisions at a community level, but to understand which approach is worth taking. Some might say its subjectivity is not appropriate for informing policy or decision making. Others seem more optimistic of its potential, in that any movement towards progress should be studied, and is worth while. This research seeks to recognize the extent to which community planning impacts our QOL, with respect to the built environment, and the role government plays in decision making.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".