Women and (Their) Children: Wrongs, Rights and Relationships
Bibliographic record
Abstract
As of the turn of the twenty-first century, a pregnant woman appears to be excluded from the Canadian private law of civil wrongs when it comes to any wrongfully inflicted harm on her own foetus. This article revisits and examines the images of pregnancy and maternity represented in the judgments in Dobson v. Dobson, a 1999 decision of the Supreme Court of Canada. Against the backdrop of contemporary discussions about surrogacy, the images put forth in Dobson – namely, the expecting woman, the autonomous woman and the woman as mother – represent more generally the co-existing and often entangled pictures with which the law grapples whenever pregnancy and maternity are at stake. This essay explores how we might imagine a more child-focused reflection of issues like those presented in Dobson. Without insisting on the applicability of children’s rights as set out in conventions or charters, it is possible to include the interests and perspective of children in a nuanced analysis of law’s engagement with pregnancy and maternity that does not compromise or erode women’s rights or interests. Indeed, a recognition of children’s interests in this context permits a juridical recognition of, and reconciling with, the complex and diverse realities of pregnancy and maternity.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.067 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".