Bibliographic record
Abstract
Previous research has consistently found that men and women have similar levels of happiness, life satisfaction, and other global measures of subjective well-being. This article demonstrates that significant gender-related differences in subjective well-being exist— but tend to be concealed by an interaction effect between age, gender and well-being. Women under 45 tend to be happier than men; but older women are less happy. Thus, in a pooled sample of 146,000 respondents from 65 societies, among the youngest group, 24 percent of the men and 28 percent of the women describe themselves as very happy; but among the oldest group, only 20 percent of the women describe themselves as very happy, while 25 percent of the men do so. The relationship between gender and well-being reverses itself, moving from a female advantage of 4 points to a deficit of 5 points. Given the huge sample size, these differences are highly significant. The aspiration-adjustment model implies that, despite their continuing disadvantages in income, status, and power, women of today should show higher levels of subjective well-being than men. A global women’s movement has been pushing for gender equality throughout the world, with some success, so that currently, women’s achievement tends to be above traditional aspiration levels. But this is offset by a systematic tendency to devalue older women. This tendency is particularly strong in advanced industrial societies where women have made the most progress—but where the mass media and advertising convey the message that only young women are beautiful and devalue the social worth of older women (Bluhm 2000). This produces an interaction between gender, age, and well-being that conceals statistically significant and theoretically interesting gender differences in subjective well-being.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".