‘Thirty years behind England’? Framing ‘natural’ childbirth in postwar Canada
Bibliographic record
Abstract
Following the North American publication of British obstetrician Grantly Dick-Read’s Childbirth Without Fear in 1944, natural childbirth theories reached new audiences, including Canadians who were interested in what they perceived as a ‘new’ way to give birth. In newspaper columns and popular titles including Chatelaine, Canadian women and experts alike discussed their perceptions of and engagement with natural childbirth ideas. In so doing, Canadian mothers and mothers-to-be articulated a range of attitudes surrounding women’s bodies and postwar gender roles. Canadian women, like their global counterparts, conceptualised their pregnancies and childbirths in various ways, demonstrating myriad understandings of what exactly constituted a ‘natural’ birth. Many women, however, regularly drew on international comparisons to position individual experiences of pregnancy, childbirth, and motherhood as ‘modern’ or ‘antiquated’. While some women continued to pathologise both pregnancy and childbirth and emphasise the need for continuous medical surveillance, others sought to position these life events as ordinary, everyday, and routine, requiring little in the way of medical intervention. Personal histories, geographic location, class, and race shaped individual perceptions of pregnancy and childbirth, fundamentally mediating Canadian women’s broader experiences of health and wellbeing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.026 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".