Bibliographic record
Abstract
Can kindness make you happier than a higher salary? According to the World Happiness Report, released each year on the International Day of Happiness, it can, CNN Health reported March 20. The report is a global analysis on happiness and well‐being in partnership with Gallup, the University of Oxford Wellbeing Research Centre and the UN Sustainable Development Solutions Network. This year's report paid special attention to acts of benevolence and people's expectations of their communities. The report divided acts of benevolence into three categories: donating money, volunteering and doing a nice thing for a stranger. Based on the data, 70% of the world's population did at least one kind thing in the last month, the report found. “We're not asking people to have unreasonably optimistic (expectations),” said Felix Cheung, Ph.D., the report's coauthor and assistant professor of psychology at the University of Toronto and Canada Research chair in Population Well‐Being. But if you can “develop that trust and you can expect that level of kindness, you will be a lot happier.” Added Ilana Ron‐Levey, a managing director of the public sector at Gallup: “Acts of generosity predict happiness even more than earning a higher salary.” The U.S. ranked No. 24 on this year's list of the happiest countries.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.164 | 0.083 |
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".