Bibliographic record
Abstract
An emerging area of subjective well-being (SWB) research is centered on the differences in the levels of SWB both across countries and among geographic regions within a country. The consideration of geographic differences would extend our knowledge about the determinants of SWB from "internal" factors of personality traits and individuals' socio-demographic characteristics to "external factors" embedded in individuals' environments. An issue with important theoretical and policy implications is whether the income of others in the same geographic area is associated with individuals' SWB. The association could be positive if people benefit from the improved resources, amenities, and social capital in high-income areas. The association could also be negative if people tend to emulate the lifestyles of their more affluent neighbours. Related empirical studies so far have not come to a consensus on this question. The present study attempts to contribute to this issue in two significant ways. First, this study examines whether the effect of the average income in a geographic area (locality income) on SWB is sensitive to the scale of geographic units. With a very large sample of survey respondents nested within three hierarchical levels of geographic areas, this study provides reliable estimates of the association of SWB with average incomes in immediate neighbourhoods (defined as "census dissemination areas"), local communities ("census tracts"), and municipalities ("census subdivisions"). Second, this study examines how the choice of control variables influences the estimated effect of locality income. By considering the effects of individual demographic and socioeconomic characteristics, self-evaluated general health, and area-level attributes in a sequential manner, it is possible to discuss the likely mechanisms through which locality income is related to individuals' SWB.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".