Bibliographic record
Abstract
This paper reports on findings from a qualitative study that examined how Canada's socio-political context influenced gestational surrogacy for same-sex male couples. Semi-structured interviews were conducted with gay fathers and gestational surrogates to investigate supports and barriers of pursuing surrogacy. Questions explored publicly available information, policies and practices of fertility clinics and hospitals, post-birth resources and cultural attitudes regarding same-sex parenthood. Findings suggest that in Canada, a global leader in LGBT rights and inclusive same-sex parenting legislation, participants encountered inadequate same-sex inclusive resources and insufficient provider competencies. The aim of this study was to inform individual and institutional recommendations to counteract biases in fertility care and post-birth services. Following interview analysis, five key strategies were identified: (1) more accessible information on paths to same-sex parenthood; (2) more inclusive fertility clinic and hospital practices; (3) recognition of same-sex fatherhood in formal documentation; (4) post-birth resources such as formula feeding, play groups and first aid courses intended for same-sex parent families; and (5) shifts in cultural attitudes of same-sex parenthood and, specifically, gay fatherhood. Approaches that subvert heteronormative discourses embedded in fertility and reproduction are required to legitimise and support same-sex parent families.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".