Helping Couples Fulfill the “Highest of Life's Goals”: Mate Selection, Marriage Counselling, and Genetic Counseling in United States
Bibliographic record
Abstract
This article traces the history of modern genetic counseling to mate selection and marriage counselling practices of the early-20th century. Mate selection revolved around a belief that human heredity could be improved and genetic diseases eradicated through better breeding. Marriage counselling, though interested in reproduction, was also concerned with the emotional and psychological well-being of couples. These two practices coalesced most obviously in the work of well-known geneticist Sheldon Reed. Even as marriage and genetic counselling diverged in the post-WWII period, vestiges of these practices remain in contemporary counseling experiences with family planning and genetic screening programs. Emphasizing points of continuity between "positive" eugenic ideologies and modern genetic practices elaborates the diverse origins of genetic counseling. It also exposes how genetic counselors have become involved in genetic enterprises beyond standard clinical settings, and prods at key issues in the interaction between genetic science and social values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".