Happiness, Space and Place: Community Area Clustering and Spillovers of Life Satisfaction in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract There has been a rapidly growing number of studies of the geographical aspects of happiness and well-being. Many of these studies have been highlighting the role of space and place and of individual and spatial contextual determinants of happiness. However, most of the studies to date do not explicitly consider spatial clustering and possible spatial spillover effects of happiness and well-being. The few studies that do consider spatial clustering and spillovers conduct the analysis at a relatively coarse geographical scale of country or region. This article analyses such effects at a much smaller geographical unit: community areas. These are small area level geographies at the intra-urban level. In particular, the article presents a spatial econometric approach to the analysis of life satisfaction data aggregated to 1,215 communities in Canada and examines spatial clustering and spatial spillovers. Communities are suitable given that they form a small geographical reference point for households. We find that communities’ life satisfaction is spatially clustered while regression results show that it is associated to the life satisfaction of neighbouring communities as well as to the latter's average household income and unemployment rate. We consider the role of shared cultural traits and institutions that may explain such spillovers of life satisfaction. The findings highlight the importance of neighbouring characteristics when discussing policies to improve the well-being of a (small area) place.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it