Composing and Recomposing Self as Lesbian Birth Mother: A Narrative Inquiry
Bibliographic record
Abstract
Motherhood and mothering are dynamic experiences, yet they are often framed within and by heterosexual contexts, positioning lesbian birth mothers as outside “typical” mothering experiences. Most pregnant women in Canada, including lesbian birth mothers receive some formalized prenatal care and give birth in an acute care setting. Despite this, little is known about the experiences of lesbian birth mothers as they interact with healthcare providers and the maternity health and social care systems. Narrative inquiry research is a way to study experience through story and is a way to generate meaningful insights into the experiences of lesbian birth mothers as they negotiate maternity care. Narrative inquiry is a relational, iterative process in which the research data collection and reporting are negotiated with participants and alongside a response community of advisors and experts. In this narrative inquiry, together with three (3) lesbian birth mothers, I explored their experiences with maternity health and social services, family, institutions, and community in the greater Calgary, Alberta area. Three co-composed narrative accounts of the experiences of being a lesbian birth mother are presented, followed by the narrative threads of (1) Lesbian Maternal Wisdom, (2) Functional Infertility or Free to Decide? (3) Shades of Grey; and (4) Whose Space is this Space? In the final chapter, implications and recommendations for practice, research, and policy are made.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.026 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".