Who Remains Unpartnered by Mid-Life in Norway? Differentials by Gender and Education
Bibliographic record
Abstract
Using data on men and women born 1927 to 1968 from the Norwegian Gender and Generations Survey (N = 8,813), we examine differentials in remaining without experience from a marital or non-marital union by age 40. We are particularly interested in differentials by gender and education, as well as changes across birth cohorts. 6.5% of the respondents (7.8% of the men and 5.2% of the women) had no union experience by age 40. Multivariate results confirmed that the odds of remaining unpartnered by age 40 decreased across the birth cohorts studied here, particularly among women. Separate models for men and women confirmed that primary educated men had the highest odds of remaining unpartnered. Among women, on the other hand, those with a university education had significantly higher odds of not having had union experience by age 40 compared with their lower educated counterparts. Results from interaction models confirmed that higher educated men have become increasingly likely to remain unpartnered. Among women, we found no evidence for a changing importance of education for remaining unpartnered by age 40.
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 | 0.002 |
| 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 | 0.001 | 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".