Bibliographic record
Abstract
2 This report explains why people living in certain regions or cities in Canada experience higher levels of life satisfaction or happiness. We make use of micro-level data from the Canadian Community Health Survey for 2007 and 2008. After a descriptive analysis of the data on happiness in Canada, the report identifies, through an econometric analysis of both individual and certain variables in a societal context, the factors that are the most statistically and economically significant determinants of individual happiness. We find that household income is a relatively weak determinant of individual happiness. Perceived mental and physical health status as well as stress levels and sense of belonging are better predictors of happiness. We then use these estimates to account for variation in happiness at the provincial, CMA (Census Metropolitan Area), and health region level, given the characteristics of the population in these geographical units. We find that the most important reason for geographical variations in happiness in Canada is differences in the sense of belonging to local communities, which is generally higher in small CMAs, rural areas, and Atlantic Canada. Résumé Nous exposons, dans ce rapport, les raisons pour lesquelles les personnes qui vivent dans certaines régions ou villes du Canada affichent des niveaux de satisfaction ou de bonheur plus élevés. Nous recourons aux
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".