Emotional labour: Exploring emotional policy discourses of pregnancy and childbirth in Ontario, Canada
Bibliographic record
Abstract
In 1991, Ontario became the first Canadian province to pass legislation establishing midwifery as a self-regulated healthcare profession and integrating it into the provincial healthcare insurance plan. Since its implementation, there has been a partial convergence of obstetric practice in the province, where, despite seemingly distinct professional philosophies of care, both midwives and physicians cohere around representations of pregnancy and birth as “normal” or “natural” life events rather than medical conditions requiring treatment. In this paper, I suggest that understanding this convergence and the effects produced by it requires an interrogation of the emotional policy discourses that shape (and are shaped by) the ways we experience the world around us. In doing so, I develop a framework for tracing the emotional policy discourses surrounding pregnancy and birth from the turn of the 20th century until the early 1990s, demonstrating that these representations reflect the merging of two emotional registers, joy and fear, where pregnancy and birth are represented as joyous, life changing events, but where joy is tempered by the fear of complications and potential tragedy. I thus show that contemporary emotional landscapes bind various “birth experts” and bracket “expertise” around particular forms of knowledge, shaping expert and maternal subjectivities along gendered, racialized, ableist, and class-based lines.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.051 | 0.047 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".