Happiness and sense of community belonging in the world value survey
Bibliographic record
Abstract
People derive considerable social benefits from interacting with others that they encounter across a host of environmental domains: their city, region, country, continent, and even the world at large. We explore the extent to which perceived happiness hinges on one's sense of connection within each domain, drawing upon a large international sample of 120k respondents from 74 nations in the World Value Survey (2017–2020). This large battery of social and political attitudes includes items that ask: “tell me how close you feel to…” followed by each of city, region, country, continent, and the world. Options ranged from ‘very close’ to ‘not very close at all.’ Perceived happiness was scored on a 4-option item asking: “taking all things together, would you say you are…” with options ranging from ‘very happy’ to ‘not at all happy.’ After accounting for various demographic variables like age, sex, education, urban/rural environment, and income, results from a stepwise ordinal logistic regression analysis showed that one's perceived happiness was predicted by feeling connected to almost all domains; and the more connected one felt, the happier they were. One's connection however to region was not significant, wherein we suspect the item was too ambiguous for a clear response. Implications for overall wellbeing are discussed, as are directions for future research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".