Twinship and Marriage – Experiences during the Course of Twin
Bibliographic record
Abstract
The aim of this study was to explore and describe older twins’ experiences of their marriages in relation to the co-twin relationship.Material and Methods: The material consisted of 34 life story interviews with older twins (70+), representing various experiences of twinship and marriage. The data was analysed with qualitative latent content analysis.Results: Phases of marriage describe the time of Courtship - partners were chosen based on infatuation. Most were non-related, but some were relatives or friends. Twinship and quality of married life showed that marriages were either disharmonious or harmonious. The most common cause of conflicts was spouses not getting along, second common cause to conflicts was the twin relationship itself. In the harmonious marriages, the spouses were sympathetic and accepting of the twin relationship. In later life 14 of 34 were widowed and 7 of 34 had gone through a divorce. The most common cause of divorce was an unfaithful spouse, rather than the close relationship with the co-twin. In difficult times the twin relationship served as a source of comfort and support.Conclusions: The most harmonious marriages were with spouses related to one another. In this way the twins could keep both the twin relationship and have a marriage. Since twins often regard each other as attachment figures, the combination of twinship and marriage seem to be a challenge for the spouse to be most of all.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".