Canadian Brides’-to-Be Surname Choice: Potential Evidence of Transmitted Bilateral Descent Reckoning
Bibliographic record
Abstract
Women’s marital surname change is important, in part, because it affects how often only husbands’ (fathers’) surnames are passed on to offspring: this, in turn, affects the frequency of these “family” names. Brides-to-be, novelly, from across especially western and central Canada (N = 184), were surveyed as to marital surname hyphenation/retention versus change intention, and attitude towards women’s such choices in general. Among women engaged to men, the hypothesized predictors of income and number of future children desired were positively predictive of marital surname retention/hyphenation under univariate analysis. Under multiple regression analysis using these and other predictors from the literature, previously found to be predictive of this DV under univariate analysis, only some of these other predictors were predictive. Of greatest predictiveness was the bride-to-be’s own mother’s marital surname choice (with brides-to-be, more often than would otherwise be predicted, following their mother’s such choice), thus suggesting a possible shift to a transmitted manner of bilateral descent reckoning, towards greater bilateral such reckoning, among a portion of the population. Reported, general attitude towards women’s marital surname retention was predictive of participant brides-to-be’s own reported (imminent) marital surname retention/hyphenation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".