Are We Happy in Densely Populated Environments? Assessing the Impacts of Density on Subjective Well-Being, Quality of Life, and Perceived Health in Montreal, Canada.
Bibliographic record
Abstract
Compact city development has been increasingly promoted as a tool to encourage urban sustainability and to reduce humans’ environmental footprint. The impacts of such urban development on subjective well-being (SWB), Quality of Life (QOL), and perceived health—non-monetary metrics of prosperity—have not been extensively explored in the North American context. This paper delves into the relationship between density and happiness by analyzing a travel survey distributed in Montreal, Quebec, Canada (n = 4,148). A cumulative logit model assessed levels of happiness—as measured by SWB, QOL, and perceived health—against confounding variables such as age, gender, household size, marital status, education, income levels, and residential self-selection, while including neighborhood density as our main policy variable. Results do not show that population density affects perceived health or SWB. However, a small inverse relationship between QOL and population density was observed. Analyzing neighborhood characteristics through their effect on SWB, QOL, and perceived health provides further evidence on the links between the urban landscape and happiness, and the study’s results can inform zoning and land-use policymaking.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".