Spouse Selection: Important Criteria and Age Preferences of an Iranian Sample
Bibliographic record
Abstract
Importance of different criteria and age preference in spouse selection for single and married young Iranian adults was examined. The sample included 104 married (49 male, 55 female) and 112 single (51 male, 61 female) students. A 26-item scale developed for this study included 4 items related to demographic factors, 3 items on preferences for the ideal age of marriage, and 19 Likert-type items asking about criteria important for spouse selection. Analysis indicated that, in Iran, commitment, chastity, refinement, and health are four important criteria for spouse selection among male and female and single and married persons. Also, experience of marriage for married males may increase maturity, social prestige, family background, having a job, and age as criteria for choosing a spouse. On the other hand, marriage experience for females may decrease the importance of social skills, housekeeping, and autonomy for selecting a spouse.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".