Learning from J.J.: An Interdisciplinary Conversation about Child Welfare, Healthcare and Law
Bibliographic record
Abstract
This paper is a collaborative project by six scholars belonging to VOICE, Views On Interdisciplinary Childhood Ethics, an inter- disciplinary group of researchers working in the field of childhood ethics. The authors consider the recent case of Hamilton Health Science Corp v DH and reflect on the story at its centre, that of JJ, an 11-year-old girl from the Six Nations of Grand River. Each author offers their perspective on the lessons that can be drawn from JJ’s story about the theory and practice of childhood ethics. The initial conflict in JJ’s story that required a ruling by the courts gave way to collaboration. Inspired by a process only made possible through meaningful conversation, this paper has been styled as three round-table conversations across disciplines. The goal of the paper is twofold: to reflect on the substantive lessons which JJ’s case might teach us, and to experiment with interdisciplinary conversations themselves. The three themes around which this paper is organized are the inclusion of childhood voices, the significance of identity and belonging, and the importance of fostering collaborative dialogue based on trust among children, their communities, and institutional actors. Each of these themes is discussed in the context of medical decision making and child protection law, and their impact on Indigenous children and communities. JJ’s story pushes us to consider the inherent opportunities and difficulties of working across disciplines as we examine complex issues at the intersection of health, law, ethics, and spirituality.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.051 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".