(Mis)Placed Justice: Justice, Care and Reforming the ‘Best-Interests-of-the-Child’ Principle in Canadian Child Custody and Access Law
Bibliographic record
Abstract
In this chapter, the author argues that placing justice at the heart of Canadian custody and access law has ethical consequences. In particular, the detached objectivity of liberal legalism’s concept of justice imposes upon families a model of family life that ignores the contextual realities of child rearing and parenting. In such a system, little space is left for recognition of caregiving or the subjectivity of actual family life. The author questions the central place of justice in Canadian custody and access law and suggests, drawing on a feminist ethic of care, that it is care, not justice, that should form the wider framework within which custody and access decision-making should take place. After explaining her use of the terms “justice” and “care” in the context of custody and access law, she discusses why the justice paradigm is flawed and why an ethic of care might be a more appropriate paradigm within which to make custody and access decisions. Consideration is given to how Canadian custody and access law might be reformed to better incorporate an ethic of care, including whether the “best-interests-of-the-child” test is capable of incorporating such an ethic, and finally whether the best interests test might need to be abandoned in favour of a model less tied to a justice framework.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.057 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| 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".